Summary – Exam Prep MBC
Lecture 2; Chapter 1 – Physicochemical Properties
Key Themes and Ideas
1. Importance of Physicochemical Properties:
o Chemical structure directly affects a drug's potency, selectivity, and ADMET
(Absorption, Distribution, Metabolism, Excretion, Toxicology) properties.
o Optimizing lipophilicity, ionization, solubility, and hydrogen bonding is crucial
for designing effective drugs.
2. Common Metrics for Compound Quality:
o Molecular weight, logP, logD7.4, pKa, aqueous solubility, hydrogen bond donors
(HBD), hydrogen bond acceptors (HBA), and polar surface area (PSA).
o "Rule of Five" highlights thresholds for poor permeability/solubility: MW > 500,
logP > 5, HBA > 10, HBD > 5.
3. Lipophilicity:
o Lipophilicity (logP/logD) is vital for drug potency and ADMET properties but
must be balanced to avoid excessive toxicity or poor pharmacokinetics.
o High lipophilicity often correlates with unfavorable drug properties.
4. Ionization Constants (pKa):
o pKa determines ionization at physiological pH, affecting solubility, permeability,
and bioavailability.
o Medicinal chemists manipulate pKa to improve drug properties.
5. Hydrogen Bonding:
o HBDs and HBAs influence drug-receptor binding and solubility. Excess hydrogen
bonding can reduce permeability.
6. Solubility and Permeability:
o Solubility is essential for oral bioavailability. Low solubility often limits drug
absorption.
o Strategies include improving crystallinity and using salts or co-solvents.
7. The "Rule of Five":
o Simplified guidelines predict oral bioavailability based on physicochemical
parameters but may not apply to "beyond Rule of Five" molecules.
8. Ligand Efficiency and Drug-likeness:
o Ligand Efficiency Metrics assess compound quality (e.g., Lipophilic Ligand
Efficiency, LLE = potency - logP).
o Drug-likeness depends on balancing physicochemical parameters for oral
bioavailability and efficacy.
Lecture 2; Chapter 2 – Synthesis in Medicinal Chemistry
Key Themes and Ideas
1. The Evolution of Synthetic Practices:
o The landscape of medicinal chemistry has shifted significantly, with synthetic
techniques evolving to accommodate high-throughput requirements and the
integration of automation.
o Historical milestones are highlighted to demonstrate how synthesis has driven
discoveries.
2. Core Synthetic Strategies:
o Four key chemical strategies to improve drug candidates:
 Methylation: Enhances potency by optimizing hydrophobic interactions
with the target (“magic methyl effect”). However, it can also increase
lipophilicity excessively, leading to off-target activity.
 Hydroxylation: Increases solubility and introduces hydrogen-bonding
interactions from HBD introduced via selective oxidation or enzymatic
transformation. Can improve PK by enhancing polarity.
 Fluorination: Modifies metabolic stability, lipophilicity, and
bioavailability – strategic fluorination or aromatic or aliphatic groups –
resistance against metabolic degradation.
 Heterocycles: Improves binding properties and physicochemical profiles
– versatile frameworks for interaction with biological targets.
3. Techniques and Innovations:
o Advancements in late-stage functionalization for modifying molecules efficiently
without synthesizing from the start.
o Biotransformations (use of enzymes to perform highly selective chemical
transformations) as eco-friendly and selective synthetic methods.
o Increased adoption of water-based chemistries to enhance sustainability (safer,
more scalable, faster).
o Use of isosteres for improved efficacy and reduced off-target effects.
4. Fragment-Based Drug Discovery (FBDD):
o Describes the utility of fragment screening to identify small, efficient binding
moieties that are then optimized into drug leads.
o Starting with small fragments (150-200 Da, grow and optimize them into larger
molecules that explore the available chemical space efficiently).
5. Avoiding Undesirable Compounds:
o Strategies for identifying and excluding potentially reactive species (PAINs –
false positives in screening assays due to nonspecific interactions) and nuisance
compounds that may interfere with assays or cause toxicity.
6. Integration of Automation and Machine Learning:
o Machine learning is emerging as a tool for synthesis planning and prediction.
o Flow chemistry and automated purification technologies streamline compound
handling.
7. Case Studies:
o Highlights practical applications of synthesis innovations, such as developing
novel leads using AMG-176 (an orally available myeloid cell leukemia (MCL-1)
inhibitor) and E7130 (a synthetic analog of natural halichondrin – a tumor
microenvironment-ameliorating microtubule inhibitor).
8. Future Directions:
o
Emphasis on synthetic methodologies to address new modalities, including
biologics and PROTACs (proteolysis-targeting chimeras).
Lecture 3 & Stryer, Chapter 2 – Introduction to protein structures
Protein Composition and Structure
Proteins are essential macromolecules in biological systems, performing a wide array of
functions, including catalysis, transport, structural support, signaling, and regulation. This
chapter explores the structural hierarchy and diversity of proteins, detailing their primary,
secondary, tertiary, and quaternary structures.
Proteins can be both the target and the drug.
2.1 Proteins Are Built from a Repertoire of 20 Amino Acids
Proteins are polymers of amino acids, which are linked by peptide bonds to form polypeptide
chains. Each amino acid features a central alpha-carbon bonded to an amino group, a carboxyl
group, a hydrogen atom, and a distinctive R group (side chain). These side chains determine the
chemical properties and behavior of the amino acids, such as being hydrophobic, polar, or
charged.
2.2 Primary Structure: Amino Acid Sequence
The primary structure of a protein is its unique sequence of amino acids, specified by genes. This
sequence determines the protein's ultimate three-dimensional conformation and function.
Polypeptide chains exhibit flexibility, yet their conformations are restricted due to the partial
double-bond character of peptide bonds, which limits rotation.
2.3 Secondary Structure: Alpha Helices, Beta Sheets, and Turns
Secondary structures arise from regular patterns of hydrogen bonding between the backbone
amides and carbonyl groups of the polypeptide chain. The two primary motifs are:
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Alpha helices: Coiled structures stabilized by intrachain hydrogen bonds. (n+4 rule, 3.6
residues per helix turn)
Beta sheets: Extended polypeptide strands linked by interstrand hydrogen bonds.
Turns and loops allow the polypeptide to change direction and contribute to the overall folding.
2.4 Tertiary Structure: Globular and Fibrous Proteins
Tertiary structure refers to the three-dimensional arrangement of a protein's polypeptide chain,
driven primarily by hydrophobic interactions. Globular proteins, such as myoglobin, fold into
compact shapes with hydrophilic residues on the surface and hydrophobic residues buried inside.
Fibrous proteins, like collagen, have extended structures and provide mechanical support.
2.5 Quaternary Structure: Assembly of Subunits
Some proteins consist of multiple (proteins) polypeptide chains or subunits. The arrangement of
these subunits is termed quaternary structure. For example, hemoglobin consists of four subunits
that work cooperatively to bind and release oxygen. They may be homomeric (multiple copies of
the same protein) or heteromeric (different proteins).
2.6 Protein Folding and Stability
Protein folding is the process by which a linear polypeptide chain assumes its functional threedimensional structure, guided by the amino acid sequence. Folding occurs through intermediate
states ("molten globules"), stabilized by hydrophobic interactions, hydrogen bonds, ionic
interactions, and van der Waals forces.
Molecular chaperones, such as Hsp70 and chaperonins like GroEL/GroES, assist folding and
prevent aggregation, especially under stress. The energy funnel model illustrates folding as a
journey toward the native state, with potential traps causing misfolding. Misfolded proteins can
aggregate, contributing to diseases like Alzheimer’s (beta-amyloid plaques) and Parkinson’s
(alpha-synuclein aggregates).
Post-translational modifications (e.g., phosphorylation, glycosylation, and disulfide bonds)
enhance stability and functionality. While denatured proteins may lose their structure under
stress, many can refold if favorable conditions are restored, highlighting the resilience of their
folding code.
…
X-ray crystallography is the most-used method for experimentally determining protein
structures, but cryo-EM is used more and more recently (NMR is also used); otherwise,
computational predictions
Difference between a peptide and protein
- Peptides are shorter
o Typically, max 50 residues
- Production in research
o Peptides are typically synthesized, while proteins are expressed in cell
lines
- Production in physiology
o Proteins are expressed from genes (via mRNA translation)
o Peptides are typically cleaved from protein precursors by enzymes
Proline – N atom lacks the hydrogen participating in the helix backbone bond
Glycine – can adapt many angles and is the most flexible amino acid because it lacks a
side chain
Domains – distinct functional/structural units in a protein – responsible for a
function/interaction
Lecture 4 & Stryer, Chapter 4.5 – Experimental Methods in Structural Biology
X-ray crystallography has helped determine the most protein structures – largest data base
- You can always use X-ray crystallography for any protein – a few atoms, small (<300
AA), or large (1000 * AA)
- Crystallization, diffraction data collection, obtaining phases, structure modelling (fitting),
refinement
Electron microscopy (EM) is picking up pace – even among those who have previously used Xray crystallography
- Structures need to reach a certain size to be able to see them with EM
- Preparation of grids, data collection/processing, structure modelling (fitting), refinement
- Take purified sample, put it on a tiny grid  add some microlites of the protein sample
NMR
- Gets too complicated to analyze large proteins
- Sample preparation, data collection, assignment of peaks & structure modelling
- Always have a size limit when using NMR – it is not commonly used for structure
determination (especially when it comes to larger proteins); however, the advantage of
NMR is that you can analyze the molecule in solution
Preferred are techniques that give an “atomic” resolution – 1.5 angstrom is preferred as you can
see where each atom is
Structural analyses require pure protein
Lectures 5 & 6 (Structure-based Ligand Design) & Chapter 3 – Useful Computational
Models for Medicinal Chemistry & Chapter 4 – Structure-based Design for Medicinal
Chemists
- Conformational changes – change to “bioactive conformation” – requires energy
- Ligand-protein interactions – polar & hydrophobic
- Ligand-protein association and dissociation constants
- Binding affinity as a difference in free energy of binding
Contributions to binding affinity:
1. Penalty for freezing the overall molecular motion
2. Conformational changes of ligand and protein (penalties)
a. Induced fit – a ligand reshapes the protein binding site
3. Electrostatic/polar interactions
a. Hydrogen bonding
4. Hydrophobic effect
a. You don’t want the ligand to be too hydrophobic as it will bind to many proteins
 become unselective (as many proteins have hydrophobic pockets, as long as
the structure of the ligand is somewhat OK and there are strong hydrophobic
interactions then it will bind)
5. Shape complementarity via van der Waals contacts
Structural biology (cryoEM & crystallography  computational drug design  chemistry 
molecular pharmacology  translational pharmacology ( new drugs))
Docking simulates binding of a ligand into a protein 3D structure – theoretical, then tested
experimentally in the lab
Post-docking filtering of compounds and poses
- SiteMap – interaction areas
o A molecule has been fitted to the binding site of the protein – different colors for
the different properties the ligand should have to bind in that specific site
o Change the way the ligand looks – double/triple bonds instead of single bonds in
the hydrophobic part – restrict rotation, keep the conformation steady  then you
can point the hydrogen bonds in the right direction and gain affinity
o Adding more polar groups – more specific binding (less off-site effects), increase
solubility
- Remove strained ligand poses (won’t fit the binding site)
- Increase diversity (so you don’t test similar compounds in the lab)
Hit/ligand optimization – analogues that can increase selectivity/potency (similar 2D structure,
shared substructures)
Computationally modeled optimization
- Binding affinity, potency, selectivity (side effects), synthetic feasibility
Experimentally tested optimization
- Toxicity, oral bioavailability, favorable PK, metabolism (first-pass), elimination
Key Topics and Techniques
1. Physics-Based vs. Empirical Models:
o Physics-based methods (e.g., quantum mechanics) study molecular behavior, such
as electronic structures.
o Empirical models (e.g., QSAR) rely on statistical relationships between chemical
properties and biological activity.
o Combining these approaches enhances prediction accuracy.
2. Quantum Mechanics and Molecular Mechanics:
o Quantum Mechanics: Used for small molecules and reactive centers to explore
electron distributions and reactivity.
o
Molecular Mechanics: Simplifies modeling for large biomolecules using force
fields to calculate molecular energy.
3. Molecular Docking and Scoring Functions:
o Docking predicts how a ligand binds to a target protein, generating multiple
binding poses.
o Scoring Functions rank poses by estimated binding affinity, though often limited
by oversimplifications.
4. Molecular Dynamics (MD) Simulations:
o Models molecular motion over time to study conformational flexibility and
dynamic binding events.
5. Applications:
o Virtual Screening: Identifies promising candidates from large compound
libraries.
o Hit-to-Lead Optimization: Guides structural changes to improve potency and
selectivity.
o ADMET Predictions: Evaluates solubility, permeability, and metabolic stability
early in the pipeline.
Emerging Trends
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Machine learning models improve activity and ADMET property predictions.
Free energy calculations (e.g., MM-GBSA) refine binding energy estimates.
Integration with structural data (e.g., cryo-EM) enhances design precision.
This chapter underscores the role of computational tools in accelerating and refining drug
discovery, complementing experimental methods.
Key Concepts and Techniques
1. Introduction to Structure-Based Design:
o Uses experimental data (e.g., X-ray crystallography, cryo-EM) to model how
drugs bind to targets.
o Advances in cryo-EM enable study of previously inaccessible targets, like large
protein complexes.
2. Protein-Ligand Interactions:
o Binding depends on complementarity between the ligand and protein binding site
(shape, charge, and hydrophobicity).
o Protein flexibility (induced fit) is considered for accurate predictions.
3. Structure-Guided Optimization:
o Iterative design involves modifying ligands based on structural data to improve
potency, selectivity, and ADMET properties.
o
Free energy calculations and molecular dynamics refine hypotheses about
binding.
4. Applications in Drug Design:
o Fragment-Based Drug Design (FBDD): Small fragments binding to the target
are identified and optimized into larger drug-like molecules.
o Hypothesis Testing: Structural data guide experiments to confirm ligand-protein
interactions and optimize lead compounds.
Emerging Trends and Tools:
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DNA-Encoded Libraries (DELs): Combine high-throughput screening with structurebased insights to identify ligands.
PROTACs (Proteolysis-Targeting Chimeras): Utilize structural knowledge to design
molecules that degrade target proteins.
Machine Learning: Enhances structure-based design by predicting binding affinities and
guiding ligand synthesis.
Lecture 7 (Enzymes) & Chapter 10 – Assays & Chapter 11 – In Vitro Biology: Measuring
Pharmacological Activity that Will Translate to Clinical Efficiency)
Enzymes are biocatalysts with many functions
- Signal transduction e.g., through phosphorylation (kinases/phosphatases)
- Metabolism (fatty acid oxidation, cholesterol biosynthesis, glycolysis)
- Epigenetics through posttranslational modifications of histones
- Degradation in the liver by cytochrome P450 (e.g., drug metabolism)
Enzymes are also drug targets
- Their function is often related to pathophysiology in diseases
- Many have well-defined pockets to be targeted by small molecules
(Henri)-Michaelis-Menten kinetics – formation of an enzyme-substrate complex
Modes of inhibition
- Catalysis – substrate binds to the active site (AS) of the enzyme
- Competitive inhibition – inhibitor mimics substrate to compete for binding to the AS
(Vmax not affected, Km affected)
- Non-competitive inhibition – inhibitor binds to enzyme or enzyme-substrate complex to
cause inactivation (Vmax decreased, Km affected)
- Uncompetitive inhibition – inhibitor binds to an allosteric site of the enzyme-substrate
complex to cause inactivation (Vmax decreased, Km increased)
Also: conformational changes, co-substrates/factors, catalytic steps
Protein degraders (e.g., PROTACs)  disease-promoting enzyme is degraded by the cell
machinery
Imatinib – targets the Brc-Abl tyrosine kinase, treats chronic myelogenous leukemia (CML)
Histone deacetylase (HDAC) inhibitors: Panobinostat, belinostat, chidamide, SAHA, romidepsin
SIRT5i against acute myeloid leukemia (AML)
Key points:
- Enzymes are important biocatalysts that can be targeted pharmacologically
- Natural products have served as rich source of inspiration
- Structure-based design efforts have proven efficient
- Inhibitors can be designed in mechanism-based fashion
- Mechanistic and kinetic insight are important
- Now binders that are not inhibitory may be turned into degraders (or PROTACs)
Key Detection Technologies
1. Absorbance-Based Assays:
o Measure changes in light absorption, primarily used for enzymatic activity
monitoring.
o Simple and cost-effective but prone to interference from compound absorption at
similar wavelengths (e.g., 330–450 nm).
2. Fluorescence-Based Assays:
o Techniques include Fluorescence Intensity (FI), FRET (Fluorescence Resonance
Energy Transfer), TR-FRET (Time-Resolved FRET), and Fluorescence
Polarization (FP).
o Widely used in high-throughput screening (HTS) for their sensitivity and ability
to measure molecular proximity and size.
3. Luminescence Assays:
o Rely on light emission from chemical or enzymatic reactions, such as luciferase
systems.
o High sensitivity and dynamic range, making them ideal for gene expression and
intracellular signaling studies.
4. Mass Spectrometry (MS):
o
A label-free method suitable for detecting minor changes in metabolites or lowabundance molecules.
o Used in various stages of drug discovery, including primary and ADMET
(Absorption, Distribution, Metabolism, Excretion, and Toxicity) assays.
5. Surface Plasmon Resonance (SPR):
o Provides real-time, label-free measurements of molecular interactions.
o Commonly used in fragment screening and to validate hits from biochemical
assays.
6. Microscale Thermophoresis (MST):
o Measures changes in molecular movement due to binding events.
o Requires minimal sample preparation and is compatible with complex biological
matrices.
Assay Types and Their Applications
1. Primary Assays:
o Designed to measure the direct activity of compounds against targets in HTS
campaigns.
o Must balance sensitivity, reproducibility, and scalability.
2. Secondary Assays:
o Validate hits from primary screens, assess potency and selectivity, and explore
mechanisms of action.
3. Biophysical Assays:
o Use techniques like thermal shift analysis and Cellular Thermal Shift Assay
(CETSA) to study protein-ligand interactions.
4. Phenotypic and Cell-Based Assays:
o Evaluate compound effects on cellular pathways and phenotypes using 3D
spheroids, organoids, or co-cultures.
o Reporter gene assays are employed to study intracellular signaling.
Assay Development and Optimization
1. Assay Design Principles:
o Focus on robustness, minimizing variability, and aligning with the target’s
biology.
o Incorporate controls to account for background signals and nonspecific binding.
2. Data Analysis:
o Emphasizes dose-response relationships and robust statistical models for
determining IC50 values.
o Poor curve fitting or variability in data necessitates re-evaluation of assay
conditions.
3. When Assays Fail:
o
o
Causes include enzyme saturation, compound instability, or limitations in
sensitivity.
Troubleshooting involves re-optimizing protocols or designing alternative assays.
Emerging Trends
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Multiplex Assays: Techniques like electrochemiluminescence and flow cytometry enable
simultaneous measurement of multiple analytes in a single well.
Automation and Miniaturization: Acoustic dispensing and advanced MS technologies
facilitate ultra-high-throughput screening, processing over 100,000 samples daily.
Biophysical Techniques: Advanced methods like SPR and MST expand capabilities for
studying challenging targets, including protein-protein interactions.
Assay Types and Applications
1. Biophysical Assays:
o Measure physical interactions between ligands and targets (e.g., SPR,
calorimetry).
o Useful for binding and target engagement studies.
2. Enzyme Assays:
o Monitor enzymatic activity or inhibition.
o Include direct and coupled assays for product or cofactor detection.
3. GPCR and Ion Channel Assays:
o GPCR assays measure secondary messengers like cAMP or calcium.
o Automated patch-clamp systems assess ion channel activity.
4. Cell-Based Assays:
o Phenotypic and reporter gene assays capture cellular responses.
o Organoid and 3D cultures improve physiological relevance.
Pharmacological Profiling
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Time-Dependent Effects:
o Capture slow-binding or tight-binding kinetics to refine dose-response
predictions.
IC50 Limitations:
o While useful, IC50 values don’t capture full mechanistic or kinetic details.
Mechanistic Studies:
o Provide insight into SARs, dose predictions, and target activity.
Challenges and Emerging Tools
1. Biological Relevance:
o In vitro systems often oversimplify in vivo environments.
o Advanced models like organoids bridge this gap.
2. Assay Artifacts:
o False results arise from nonspecific interactions; rigorous controls mitigate these
risks.
3. Emerging Technologies:
o High-content imaging, CRISPR-based models, and microfluidics enhance
accuracy and physiological relevance.
Key Takeaways
In vitro assays are essential for understanding target activity and pharmacology. Their reliability
depends on robust design, proper controls, and integration with advanced technologies for better
clinical translation.
Lectures 8-10 (AI in Drug Discovery) & Chapter 6 – Machine Learning in Drug Design
Difficulty with drug development – many combinations, once you change one thing you change
everything (the whole molecule – all its properties) – need to make sure it is safe and efficacious
Each 0 and 1 – describes a specific group,
we can thus simplify the molecule’s properties
MACCS
The network can learn to recognize patterns
Want to predict if this is a good/bad molecule, or a specific value – clearance, solubility, etc.
Two classes of molecules – actives and inactives
Give feedback to the model regarding correct/incorrect predictions
Then giving the algorithm some compounds,
it can then predict if it is a good or a bad molecule
AI is hot, but also facing reality; it is all about patterns – in itself, it does not understand
chemistry; it can reduce lab work; enables us to explore a large chemical space; if you give AI
enough data of enough high quality, then it will learn and make correct predictions
-
Multiparameter optimization – we want to predict all these properties
Looking at different synthesis route/reactions that can work – create massive libraries,
then create virtual synthesis
Virtual synthesis – key components (building blocks) + synthesis rules-what/how to fuse
Huge libraries; when filling up unknown space – start broad, then narrow down specifics
Encoding the molecule into the matrix
SMILES
-
Needs to understand the branches, the relationships between
rings, etc. – thus you need the long/short-term memory
Property predictions (e.g., absorption, solubility) – limited by training set, chemical space
close to training set; not trained for new chemical groups; input determines output
AI – can’t really predict things it hasn’t seen before – applicability domain
AlphaFold predicts protein structures from sequences
Start with noise (no form/shape),
then starts taking shape and becomes
a protein
Generative models for biologics (RF diffusion) – input is a known protein, output is a de novo
protein/peptide ligand – able to create (sub-) nanomolar affinities
1. Introduction to Machine Learning in Drug Design
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ML has been applied in drug discovery for over two decades, with a significant rise in the
use of deep learning (DL) methods in recent years.
DL models, built on artificial neural networks (ANNs), excel in modeling complex,
nonlinear relationships in data.
The chapter emphasizes the role of DL in managing the growing complexity and volume
of chemical and biological datasets.
2. Applications of Machine Learning
1. Generative Models for Molecular Design:
o SMILES-Based Generative Models: Use simplified molecular representations to
create novel molecules.
o Graph-Based Generative Models: Build molecules atom-by-atom or bond-bybond, mimicking the manual structure-drawing process.
2. ADMET Prediction:
o ML models predict Absorption, Distribution, Metabolism, Excretion, and Toxicity
(ADMET) properties.
o Techniques include:
 Deep Neural Networks (DNNs): Model complex relationships in ADMET
datasets.
 Convolutional Neural Networks (CNNs): Initially designed for image
recognition, now applied to molecular property prediction.
 Graph Neural Networks (GNNs): Utilize molecular graphs for better
ADMET modeling.
3. Virtual Screening:
o Combines ML with molecular docking and free energy calculations to predict
binding affinities.
o Techniques like QSAR (Quantitative Structure–Activity Relationships) are
enhanced by DL models for large-scale database screening.
4. Synthetic Route Prediction:
o Uses AI to predict retrosynthetic pathways based on reaction templates derived
from databases like Reaxys.
o Advanced models integrate Monte Carlo Tree Search to score and rank synthetic
routes.
3. Challenges and Limitations
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Data Quality: ML models require high-quality, curated datasets to perform effectively.
Overfitting: Models may excel on training data but struggle with generalization.
Integration Challenges: Aligning ML predictions with experimental validation remains
complex.
4. Emerging Trends and Future Directions
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Federated Learning: Enables collaborative model training across organizations without
sharing proprietary data.
Automation: Systems like Chemputer integrate ML models into automated synthesis
platforms.
Precompetitive Collaborations: Enhance dataset accessibility and diversity for training
more robust ML models.
Lecture 11 & Chapter 7 – Drug Metabolism
Bioavailability – how much of the drug becomes available at the site of action
In (drug)  patient  Out (clearance – the rate of removal of the drug from the body &
metabolism – biotransformation of drugs in the body)
All other organs are also metabolizing the drug
(not only the liver and kidney) – even the eye has
a way to metabolize/clear the drug (effectively)
Liver – main machinery for transforming
foreign compounds
Bioactivation of drugs or prodrugs to reactive metabolites/intermediates (COOHs form covalent
adducts)
Adding on a
polar, sugar-like
group – the drug
is more soluble
Formation of reactive metabolites may not be a problem – many drugs don’t form that much of
them – drugs that are tested positive for bioactivation and have caused drug-induced liver injury
(DILI) are characterized by high dose compared to those drugs that appear to be safe despite
some reactive metabolite formation in vitro.
Drug-drug interactions (DDIs) – one drug influences the bioavailability of another, may lead to
unwanted effects; idiosyncratic drug reactions (IDR) – adverse reaction that does not occur in
most patients treated with a drug and does not involve the therapeutic effect of the drug
Lecture 12 & Chapter 8 – ADME Optimization in Drug Discovery
Bioavailability – extremely complex property, influenced by numerous processes in the body
You want to find a balance with a high enough LogP
to be able to pass a membrane, but also
a high enough LogS so that the drug is soluble enough
It can be predicted fairly
well using computer modelling
The target defines the LogP we may need
Absorption and permeability depend on
solubility and lipophilicity – BCS
Lipinski’s rule of 5
Several classes of large molecules are (surprisingly)
orally active – mostly coming from natural products
Conformational effect – molecules may adopt
conformations hiding the polar atoms for
solvent exposure; flexible shielding
of amide bonds enhances cell permeability;
molecular chameleons
Optimization and Prediction of PK Parameters
1. Oral Absorption:
o Aim for ≥50% absorption and ≥30% bioavailability to reduce variability in
clinical response.
o Avoid compounds that are substrates for efflux transporters (e.g., P-glycoprotein).
2. Volume of Distribution (Vd):
o Optimize lipophilicity and plasma protein binding to achieve desired tissue
distribution.
3. Clearance:
o Predict hepatic clearance using in vitro models (e.g., microsomes, hepatocytes)
and PBPK modeling.
4. CNS Distribution:
o CNS drugs require tailored physicochemical properties (e.g., molecular weight
<450 Da, logP 1-3).
New Modalities and Challenges
1. PROTACs (Proteolysis-Targeting Chimeras):
o High molecular weight and polarity pose unique ADME challenges, requiring
novel predictive models.
2. Alternative Routes:
o Inhaled, topical, and intravenous drugs bypass first-pass metabolism, requiring
distinct optimization strategies.
Lectures 13-14 & Chapter 27 – New (Therapeutic) Chemical Modalities
Drug modalities – small molecules, peptides, proteins, antibodies, oligonucleotides, DNA &
RNA, etc.
Small molecules – compounds with low MW capable of modulating biological processes to
diagnose, treat, or prevent diseases
-
-
Peptides – up to 150 amino acids, can be made synthetically; proteins – bigger, usually
expressed cellularly
Deciding on a drug modality based on the target we want to influence (receptors –
GPCRs, ligand-gated ion channels, etc.), the mechanism of action we would like, the
disease – thus the administration route as well
From 2009 to 2019 – change towards a lot more antibodies being popular/selling well
Novo Nordisk – major drug modality in their R&D pipeline is DNA/RNA
When a drug goes off-patent, it completely disappears off the top selling drugs list
Post-translational modifications (PTMs) – the human body “decorates” a protein 
glycosylation, action by kinases, etc.
Natural products  anti-infectants, treatments for cancer
Biggest challenge of
making peptides/proteins
as oral drugs – costeffective production (a
drug modality here is key)
-
Proteins – have to be kept in the fridge because otherwise they might degrade
The more non-human the monoclonal antibodies are, the higher the risk of
immunogenicity
New Modalities (NMs) aim to tackle unmet medical needs by addressing hard-to-drug targets,
such as transcription factors and protein–protein interactions. These modalities leverage
advancements in biology and chemistry to redefine the therapeutic landscape, transitioning
undruggable targets into druggable ones.
Chemical Architectures of New Modalities
NMs span beyond traditional small molecules and biologics to include nucleic acids, lipids,
carbohydrates, and hybrid architectures. Significant categories include:
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Oligonucleotides (ONs): DNA or RNA-based therapeutics such as antisense
oligonucleotides (ASOs), small interfering RNAs (siRNAs), and aptamers. They interact
with genetic materials to influence protein expression and require modifications (e.g.,
phosphorothioates) for enhanced stability and pharmacokinetics.
Peptides: Cyclic and polycyclic peptides provide high specificity and stability.
Innovations include stapled peptides for protein interaction targeting and hybrid designs
for cellular uptake and improved efficacy.
Hybrid Modalities: Fusion of small molecules with peptides, ONs, or antibodies
enhances pharmacokinetics, binding affinities, or MOAs through chemically-induced
proximity.
Modes of Action
NMs introduce new mechanisms to modulate targets across DNA, RNA, and protein levels.
1. DNA-Level MOAs: Genome editing with CRISPR-Cas9 and other strategies allow direct
genetic correction.
2. RNA-Level MOAs: ONs and siRNAs alter gene expression by degrading RNA or
modulating splicing.
3. Protein-Level MOAs: Targeted protein degradation through heterobifunctional
degraders (e.g., PROTACs) uses E3 ligase recruitment for proteasomal degradation.
Other modalities include hydrophobic tagging, autophagy-targeting chimeras (AUTACs),
and lysosome-targeting chimeras (LYTACs).
Hit Discovery and Optimization
Hit Finding Strategies


Screening Methods: Techniques such as high-throughput screening, DNA-encoded
libraries, and machine learning guide hit identification.
Structure-Based Design: Used extensively for designing hybrid molecules or enhancing
PROTAC efficacy.
Optimization Challenges

Pharmacokinetics (PK): NMs often have high molecular weights and complex
properties, demanding innovative approaches to improve stability, solubility, and
bioavailability.

Delivery Methods: Advances in lipid nanoparticles (LNPs) and other carriers help
improve delivery efficiency, especially for ONs.
Selection Framework
The selection of modalities is context-specific:


Factors include target tissue, MOA requirements, and therapeutic goals.
Example: LYTACs target extracellular proteins, while PROTACs work intracellularly.
Clinical Progress
NMs have led to marketed drugs like siRNAs (Patisiran) and ASOs (Nusinersen). Innovations
such as RIBOTACs (RNA-targeting degraders) and molecular glues (e.g., Indisulam) further
expand therapeutic opportunities.
Lecture 15 & Chapters 10-11 – Pharmacological Activity and Assays
FBDD – fewer, smaller compounds are tested
Think about an assay cascade to
find and validate hits – which
should not turn out to be falsepositives; Secondary assays –
more/another level of
information
Non-specific binding to proteins
Page 9 of this doc
for textbook
chapters
Lecture 16 & Chapter 5 – Fragment-based Ligand discovery
Challenges in drug design – finding the right target (complicated biology), real testing in humans
– very late, optimization of drugs – many parameters
Once fragment hits are identified, they are optimized to improve potency and drug-like
properties:
- Growing: Adding functional groups to strengthen interactions.
- Linking: Connecting multiple fragments that bind adjacent sites.
- Merging: Combining overlapping fragments into a single molecule.
Lectures 17-18 & Chapters 10-11 – Receptors and Ion Channels
Page 9 of this doc for summary of the textbook chapter
-
Receptor activity regulates or influences almost all human physiological functions
Receptors are the primary target for drugs to treat disease caused by abnormal functions
Lectures 19-21 & Chapter 13 – Bioinformatics for Medicinal Chemistry
How to calculate similarity
-
Similarity calculation – substitution matrix – how close a genetic snippet of humans is to
other species – the bigger the number, the more different the snippet is
Phylogenetics is important for classification of species, forensics, identifying pathogens,
etc.
Accepted point mutations (PAMs) – tracks accepted mutations, predicts how sequences will
evolve
-
Using large genomic data sets to interpret human genetic variation
Genetic catalog – all significant findings in terms of genetic makeup that is related to
some diseases
Lectures 22-23 & Chapters 14 & 17 – Translational Science & Innovation and Patents
Bench (basic biomedical research) - bedside (clinical research) - community (improved health)
Intellectual property rights are the rights given to legal persons over the creation of their minds
They usually give the creator an exclusive right over the use of her/his creation for a certain
period of time
IPR – can be a physical design, work of art, brand, trade secrets (NDA, MTA), technology
An invention needs to be novel, inventive, and enabled to be patented
Key Themes:
1. Definition and Scope:
o Defined by the NIH as turning observations into health interventions.
o Encompasses phases from preclinical discovery to clinical development and
community outcomes.
2. Challenges in Drug Discovery:
o The pharmaceutical industry faces low success rates (~5%) and long timelines
(13-15 years).
o High-quality translational research is critical to overcome these challenges.
3. Success Factors for Drug Discovery:
o Target Identification and Validation:
 Success increases with a deep understanding of disease mechanisms and
proof-of-mechanism studies in preclinical models.
 Rare genetic diseases and oncology have higher success rates due to
identifiable genetic targets.
o Patient Selection:
 Biomarkers are crucial for linking targets to disease subtypes and selecting
appropriate patients for clinical trials.
o Holistic Line-of-Sight:
 Collaboration across research, clinical development, and patient advocacy
is essential for clear pathways from research to community outcomes.
4. Emerging Technologies and Innovations:
o Genome editing and organ-on-chip models are revolutionizing disease modeling.
o Spatial single-cell sequencing allows detailed molecular analysis at the cellular
level.
o Artificial intelligence and real-world evidence analysis enable better target ideas,
biomarkers, and patient stratification.
5. Biomarker Science:
o Biomarkers are essential for patient stratification, measuring drug efficacy, and
understanding disease mechanisms.
o Digital biomarkers, including wearable technologies and data-driven tools, are
transforming drug discovery and development.
6. The Role of Data Science:
o AI and data science support translational research by analyzing large datasets
from clinical trials and population studies.
o Data integration aids in sustainable drug development based on real-world
evidence.
17.1 Introduction


Patents are a cornerstone of the pharmaceutical industry, incentivizing innovation by
granting a period of market exclusivity.
Without patents, generic competitors could undermine profitability, discouraging
investment in expensive R&D efforts.
17.2 What is a Patent?


A patent provides legal protection for inventions that meet specific criteria.
In exchange for public disclosure, the inventor is granted exclusive rights for a limited
time.
17.3 Conditions for Patentability
To qualify for a patent, an invention must meet the following criteria:
1.
2.
3.
4.
Novelty: The invention must not have been disclosed publicly before.
Inventive Step: It should not be obvious to someone skilled in the field.
Industrial Applicability: The invention must have practical utility.
Exclusions: Certain categories, such as natural discoveries, are not patentable.
Additionally, patents must describe the invention clearly and provide enough detail for others to
replicate it.
17.4 Anatomy of a Patent Specification
A patent consists of several sections:



Description: Details of the invention.
Claims: Define the legal scope of protection.
Case studies demonstrate the importance of experimental data in supporting inventive
steps and sufficiency.
17.5 Ownership and Inventorship


The inventor or the entity employing the inventor owns the patent.
Correct attribution of inventorship is crucial to avoid legal challenges.
17.6 Process for Obtaining a Patent



Patents are jurisdiction-specific but follow similar procedures globally.
The application process involves filing, examination, and potential amendments.
Costs and strategic considerations, such as choosing jurisdictions, are highlighted.
17.7 Post-Grant Considerations



Maintenance: Patents require periodic fees to remain active.
Extensions: Possible for specific pharmaceuticals to compensate for regulatory delays.
Challenges to Validity: Competitors may contest patents through legal channels.
17.8 Use of Patents



Infringement and Enforcement: Patent holders can enforce rights through litigation.
Licensing: Allows others to use the patent under agreed terms.
The Patent Box: Tax incentives for income derived from patented inventions.
17.9 Generic Medicines and Barriers to Entry

Generic drugs significantly reduce costs but face hurdles such as:
o Regulatory Data Exclusivity: Delays in generic entry to protect innovator data.
o Technical Challenges: Complexities in reproducing certain formulations.
17.10 Patents as a Source of Information

Patent databases are a rich resource for technological and competitive intelligence.
Lecture 24 & Chapter 18 – Target Validation
Target identification – discovery of potential target associated with a disease vs Target
validation – proving that modulating a target can affect disease progression in a meaningful and
safe way
There isn’t a single method to validate all targets – there needs to be a combination of tests,
evidence, etc.
Not every mutation on the gene is equal,
some may not elicit a change in amino acid
synthesis/function
Looking at the overlap between druggable
targets and those which are involved in
certain disease states
Diseased vs non-diseased state, looking at differences in the
expression of genes/RNA sequencing, looking at pathology find proteins that are over-/under-expressed
18.4 Functional Cell Systems – Assessing ‘Differential Patient Biology’

Advanced cell systems mimic patient-specific biology, aiding target validation:
o Co-culture Systems: Mimic cell-cell interactions.
o Environmental Stimuli: Recreate disease microenvironments.
o Organoids: 3D cell cultures replicate organ function.
o Microphysiological Systems: Miniaturized models of human organs.
o Cell Source: Stem cells and patient-derived cells offer personalized insights.
18.5 Ex Vivo Tissue Systems

Use of tissues from animal models or human donors for validation:
o Precision-Cut Tissue Slices: Maintain tissue architecture and function.
o Isolated Organs: Provide whole-organ responses.
Lecture 25 & Chapters 19-20 – Lead Generation and Optimization
You can start with some kind of knowledge – e.g., a natural product – know what beneficial
readouts it had
Say you want to do unbiased screening – don’t want to know anything about the molecule, or
you don’t know where it binds – how to find a starting point?
In silico screening – generate compound on the computer – but cannot do them all; maybe
screen for something which is easy to synthesize, then prove your computer model
right/wrong
Focused screening – have to know something about the target
-
Want to optimize many parameters – very
difficult to do
Hit identification by HTS
- Compound library (small, medium, large, campaign size) (3k to 2 million)
- Focused library (biased – something known about the target or ligand)
- De novo synthesis: How do we select chemically “diverse” compounds as starting points?
Also referred to as “chemical diversity”
Chemical diversity
- How to group compounds and screen one from each group – usually choose one
randomly from each group – if two companies are doing the same thing, they could be
testing different compounds
- Biased vs unbiased search/parameters – the target is highly biased, only binds some
compounds (maybe it wants a negative charge, etc.) – it is an advantage to know even a
little bit about the target
- There is no such thing as chemical diversity – we can talk about chemical similarity (a
starting point with similarities)
-
-
If the small molecule was continuous in its behavior, we could predict things;
however, in structure-activity relationship studies, we have seen that there is
discontinuity
Increasing size to a certain point – doesn’t give positive feedback, but maybe if we
keep going, we will increase affinity with an even bigger group – maybe opening up of
a big pocket - induced-fit thinking – difficult or even impossible to predict, but it is
very common
-
Every time you add a new group, the molecule may conform to a new binding mode which
is favorable
Observing some problems
Mutually induced fit – both the substrate and the enzyme conform to one another
Knowledge-based, it is not a random guess – where does the problem come from
and what chemical change can I do to fix it
We are good at calculating enthalpy (H), but we cannot calculate entropy (S) well
See if you can do something to the molecule so it adapts a new pose and creates
more hydrogen bonds
Couldn’t have found penicillin by virtual screening – because of a chemical
reaction (it is a covalent binder) – wouldn’t end up as a hit, also has quite a
complex chemistry
Paclitaxel – would never consider it in a normal search (because of its huge size)
– underlines how important natural products are as a starting point
No database can cover all possible small-molecule drugs, they are
discontinuous – every single molecule is unique
If you don’t know anything about your target – de-novo drug design – have
a natural ligand, but it is not druggable – e.g., too polar, etc. – don’t know what pocket
you need to target, etc. – very difficult, usually not worth pursuing – but you can get
lucky
Want to optimize compounds for a lot of different parameters (properties) –
have to learn along the way (guest-host interactions)
The more you can, define the ideal parameters around the compound that
you want – it very much depends on where it works, how
Don’t have unlimited resources – when do you stop? (hoping for a lucky
breakthrough)
19.2 Hit-Finding Approaches
19.2.1 Knowledge-Based Approaches


Leverages existing information on targets and compounds for focused discovery.
Employs cheminformatics and structure-activity relationship (SAR) data.
19.2.2 High-Throughput Screening (HTS)


Screens large compound libraries for activity against a target.
Key for uncovering novel hits.
19.2.3 DNA-Encoded Library Screening


Utilizes DNA tags to encode libraries of millions of compounds.
Streamlines identification and selection of potential hits.
19.2.4 Focused Screening

Targets subsets of libraries based on prior knowledge of similar compounds.
19.2.5 In Silico (Virtual) Screening


Employs computational tools to predict interactions between compounds and targets.
Reduces experimental costs.
19.2.6 Fragment-Based Lead Generation


Starts with small molecular fragments.
Optimizes fragments into lead compounds through structure-based drug design.
19.2.7 Phenotypic Drug Discovery

Focuses on observing effects in cellular or organismal models without pre-selecting a
molecular target.
19.2.8 Integrated Approaches

Combines multiple methods to increase success rates and diversify lead structures.
19.3 Hit to Lead



Translates initial hits into lead compounds through optimization.
Emphasizes improving potency, selectivity, and drug-like properties.
Incorporates ligand efficiency and lipophilic ligand efficiency metrics.
19.4 Conclusion


Lead generation is a multidisciplinary process combining biology, chemistry, and
computational techniques.
Success depends on strategic planning, high-quality assays, and efficient integration of
hit-finding methods.
19.5 Hints and Tips




Define a clear drug candidate profile early.
Use relevant, high-quality assays.
Combine complementary hit-finding methods for robust results.
Prioritize low molecular weight and low lipophilicity compounds as starting points.
Key Objectives of Lead Optimization
1. Enhance Potency and Selectivity: Improve binding affinity to the target while
minimizing off-target interactions.
2. Optimize Pharmacokinetics (PK): Achieve favorable absorption, distribution,
metabolism, and excretion (ADME) properties to ensure efficacy and safety.
3. Address Toxicity: Mitigate potential toxicity risks early through predictive tools and
structural modifications.
4. Consider Developability: Focus on solubility, stability, and scalability of synthesis for
clinical and commercial feasibility.
Strategic Considerations in Lead Optimization
1. Iterative Design and Testing:
o Utilize structure-activity relationships (SAR) to systematically improve the
compound.
o Incorporate feedback loops from in vitro and in vivo studies to refine properties.
2. Balancing Multiple Parameters:
o Simultaneously optimize efficacy, PK, and safety.
o Employ multiparameter optimization tools to navigate trade-offs.
3. Early Risk Assessment:
o Predict and address potential liabilities, such as hERG inhibition, cytochrome
P450 interactions, and reactive metabolites.
Techniques and Tools
1. Computational Methods:
o Leverage molecular modeling, QSAR, and virtual screening for informed design.
o Use machine learning and AI to analyze large datasets and predict outcomes.
2. High-Throughput Screening (HTS):
o Rapidly test compound libraries for activity against the target and evaluate
ADME properties.
3. Biophysical and Structural Approaches:
o Use X-ray crystallography and NMR to study target-ligand interactions.
o Apply fragment-based drug design to explore chemical space.
4. In Vivo Models:
o Assess efficacy, PK, and safety in animal models as predictors of human
outcomes.
Challenges in Lead Optimization



Attrition Rates: High failure rates in late-stage development due to unresolved PK or
toxicity issues.
Property Inflation: Overemphasis on potency may lead to poor drug-like characteristics,
such as low solubility or permeability.
Time and Resources: The iterative nature of optimization requires significant investment
and expertise.
Success Stories and Case Studies

Highlighted examples demonstrate how integrated approaches and innovative strategies
have transformed promising leads into successful drugs.
Hints and Tips



Begin with a clear target product profile (TPP) to guide optimization efforts.
Maintain open communication across disciplines to ensure alignment of goals.
Continuously evaluate lead compounds using both experimental and computational tools.
Lectures 26-27 & Chapter 9 – Molecular Biology for Medicinal Chemists
Start out with a sample of DNA you want to amplify – have to have short
primers (stretches of DNA) that bind to the 5’ and 3’ ends
Add the nucleotides with the primers, heat to denature, then annealing and
elongation
Large sequencing capacity is needed if you want to do large (full) genome sequences (e.g., of humans)
Need a large amount of the plasmid
Use of the cell machinery to express the protein of
interest
E. coli – the “work horse” – fast, cheap, effective, start with it, if it can’t express larger,
more complex mammalian proteins – continue with insect cells (which however are
much slower) – if that doesn’t work, then try with transient HEK293 cells
interest
1. Fundamental Molecular Biology Concepts



Central Dogma of Molecular Biology: DNA serves as the template for RNA, which
subsequently translates into proteins. This flow is fundamental to drug discovery, as most
drugs target proteins or the processes regulating them.
Gene Expression: Regulation of transcription and translation offers potential drug
targets, such as transcription factors and epigenetic modifications.
Genomics and Proteomics: Technologies like genome sequencing and proteomics
facilitate identification of disease-associated targets and biomarkers.
2. Drug Targets in Molecular Biology



Proteins as Targets: Enzymes, receptors, ion channels, and structural proteins remain
primary drug targets. Drugs often modulate protein activity to restore normal function or
block pathological mechanisms.
Nucleic Acids as Targets: DNA and RNA can be directly targeted using antisense
oligonucleotides, RNA interference (RNAi), and CRISPR-Cas9 systems. These strategies
are increasingly explored for diseases linked to specific genetic mutations.
Epigenetics: Changes in DNA methylation and histone modifications are druggable
pathways, especially in cancer and neurodegenerative diseases.
3. Techniques in Molecular Biology Relevant to Medicinal Chemistry




High-Throughput Screening (HTS): Molecular biology contributes to the design of
assays for HTS, enabling rapid identification of lead compounds.
Recombinant DNA Technology: Used to produce proteins and enzymes for screening or
therapeutic applications.
CRISPR and Gene Editing: Offer precision in modifying disease-related genes,
providing avenues for novel therapeutic strategies.
RNA Technologies: RNA therapeutics, such as small interfering RNAs (siRNAs) and
mRNA vaccines, have gained prominence, especially in addressing previously
undruggable targets.
o siRNA is a short, double-stranded RNA molecule (typically 20-25 base pairs
long) that plays a role in regulating gene expression.
o It guides cellular machinery to a specific mRNA (messenger RNA) molecule that
matches its sequence and promotes its degradation. This process silences the
expression of the corresponding gene.
o siRNA is widely used in research and therapeutics to knock down the expression
of disease-related genes.
4. Tools for Target Validation

Gene Knockdown/Knockout Models: RNAi and CRISPR technologies are used to
confirm the relevance of targets in disease models.
o RNA interference is a natural cellular process that uses small RNA molecules
(like siRNA or miRNA) to regulate gene expression by degrading specific mRNA
molecules.
o


RNAi begins with the introduction of siRNA (or similar molecules), which are
incorporated into a protein complex called RISC (RNA-induced silencing
complex). RISC uses the siRNA as a guide to bind to the complementary mRNA,
leading to its cleavage and degradation.
o RNAi is a defense mechanism against viruses and transposons, as well as a key
regulatory system for controlling gene expression.
Proteomics and Transcriptomics: Allow the study of protein and RNA expression
profiles to validate therapeutic hypotheses.
Animal Models: Genetically engineered models mimic human diseases, enabling
preclinical evaluation of potential therapies.
5. Challenges and Opportunities



Target Specificity: Developing drugs that selectively affect diseased cells without
harming normal ones is a key challenge.
Emerging Technologies: Advances in single-cell sequencing, structural biology (e.g.,
cryo-EM), and computational biology are expanding possibilities for innovative drug
design.
Translational Applications: The gap between molecular findings and clinical success
remains a hurdle; integrating molecular biology with clinical data is essential.
6. Case Studies The chapter includes real-world examples of how molecular biology principles
are applied in medicinal chemistry, such as:



Development of kinase inhibitors targeting specific cancer mutations.
The use of monoclonal antibodies against protein targets like cytokines in inflammatory
diseases.
Success stories of RNA-based therapeutics, exemplified by mRNA vaccines.
Class 1+2 – Keap1-Nrf2 Protein-Protein Interactions
Class 3+4 – Enzymes and cancer - HDACs
1. Describe the fluorescence-based HDAC inhibition assays.
Fluorescence-based HDAC inhibition assays measure the enzymatic activity of HDACs by using
fluorogenic substrates. HDAC enzymes remove acetyl groups from lysine residues, exposing a
functional group that reacts with a developer to produce fluorescence. The intensity of
fluorescence correlates with HDAC activity, and inhibitors reduce this signal. These assays are
commonly used to determine IC₅₀ values (concentration of inhibitor required to reduce activity
by 50%) for HDAC inhibitors.
2. Describe the common structural elements (units) in the chemical structures of HDAC6
inhibitors.
HDAC6 inhibitors typically consist of:




Zinc-Binding Group (ZBG): Usually a hydroxamate moiety that chelates the Zn²⁺ ion in
the active site.
Linker: A hydrophobic region connecting the ZBG to the cap group and fitting into the
HDAC’s hydrophobic tunnel.
Cap Group: A bulky moiety that interacts with the enzyme's surface residues, enhancing
selectivity and binding affinity.
Connecting Unit (CU): Links the cap to the linker and may influence selectivity.
3. Based on Table 1, modification of what unit is most effective in selectivity and potency?
The cap group plays a critical role in both selectivity and potency. Modifications to the cap,
such as introducing bulky aromatic groups (e.g., dichlorophenyl moieties), enhance interactions
with the enzyme’s surface (e.g., the L1-loop pocket). This improves selectivity for HDAC6 over
other isoforms.
4. How are SI, LE, and LipE defined? How are these parameters used in a selection
process? Compare to the endogenous substrate.



Selectivity Index (SI): Ratio of IC₅₀ values for HDAC6 versus other isoforms (e.g.,
HDAC1). Higher SI indicates greater selectivity for HDAC6.
Ligand Efficiency (LE): Defined as 1.4 × pIC50/number of heavy atoms. Measures
binding efficiency relative to molecular size.
Lipophilic Ligand Efficiency (LipE): Calculated as pIC50−clogP. Balances potency
with lipophilicity, favoring compounds with lower lipophilicity but high binding affinity.
These parameters help prioritize compounds with optimal potency, selectivity, and drug-like
properties. Compared to the endogenous substrate (e.g., acetylated α-tubulin), selective inhibitors
like SS-208 achieve higher specificity and reduced off-target effects by targeting HDAC6specific residues.
Class 5+6 – Peptides and ion channels
1. MAPS: What is the scientific rationale behind their selection of amino acids? Can you
propose another set of amino acids?
The Multi Attribute Positional Scanning (MAPS) approach systematically substitutes specific
amino acids across the peptide sequence to probe their role in potency, selectivity, and activity.
The selected substitutions in the study include alanine (small size), 1-naphthylalanine (bulky and
hydrophobic), glutamic acid (acidic), arginine (basic), and lysine (basic). These substitutions
reflect a range of physicochemical properties to identify residues critical for Kv1.3 selectivity
and activity.

Proposed additional set: Consider incorporating tryptophan (bulky and aromatic),
histidine (polar and pH-dependent), and serine or threonine (hydroxyl-containing for
potential hydrogen bonding).
2. Disulfide bond formation: What is the oxidant, and how can it be checked whether the
right S-S bonds are formed?


Oxidant used: The study likely used air oxidation or chemical oxidants like iodine or
oxidized glutathione for disulfide bond formation.
Checking correct S-S bonds: Analytical methods include:
o Mass Spectrometry (MS): To confirm the molecular weight matches the
expected folded form.
o NMR Spectroscopy: To verify the correct 3D conformation of the peptide.
o Functional Assays: To ensure the peptide retains its expected biological activity
(e.g., Kv1.3 inhibition).
3. The selectivity for Kv1.3 vs Kv1.1 is 5. When can a compound be considered selective?
A compound is generally considered selective if its selectivity index (SI) (e.g., the IC₅₀ ratio for
off-target vs. target) is sufficiently high, typically ≥10-fold, depending on the therapeutic
context. In this study, a selectivity of 5 is moderately selective but may not be ideal for
therapeutic use without further optimization to reduce off-target effects.
4. Some peptides were not isolated due to technical problems. What is the scientific
problem behind this?
The failure to isolate certain peptides likely stems from challenges in disulfide bond formation or
folding. Peptides with multiple cysteines (like ShK with three disulfide bonds) can misfold,
forming incorrect disulfide bonds. This results in heterogeneous mixtures that are difficult to
purify.

Solutions: Optimize the folding conditions (e.g., pH, redox buffer systems) or use
mutagenesis to simplify the disulfide framework.
Class 7+8 – Neurotransmitter transporters & depression
1. Thermostabilizing mutations: Explain how mutations can stabilize the structure. You
may involve △H and △S in your argumentation. Also, how can such mutations be
designed?
Mutations stabilize protein structures by decreasing entropy (ΔS) of the unfolded state or by
increasing enthalpy (ΔH) of the folded state through enhanced interactions such as hydrogen
bonds, ionic interactions, or van der Waals forces. For example, mutations such as Ile291Ala and
Thr439Ser in the serotonin transporter increased its stability in detergents, facilitating
crystallization. Such mutations can be designed using high-throughput ligand-binding assays,
computational modeling, or site-directed mutagenesis targeting residues with potential
destabilizing effects.
2. In drug discovery, what is in general the importance of the identification of allosteric
binding sites?
Identifying allosteric sites is crucial because allosteric modulators can regulate protein activity
without directly competing with the endogenous ligand at the active site. This provides
opportunities to design drugs with enhanced specificity, reduced side effects, and novel
mechanisms of action.
3. Why did the researchers use Fab fragments to achieve high resolution structures? What
is the beneficial role of the Fab fragment?
Fab fragments improve the resolution of crystallographic structures by stabilizing flexible
extracellular regions of the target protein, reducing conformational heterogeneity. In the
serotonin transporter study, the Fab fragments bound to extracellular loops, enhancing crystal
lattice packing and facilitating structure determination at higher resolution.
4. Are S-citalopram (Fig. 2c) and paroxetine (Fig. 2e) unambiguously docked in the
electron density?
The study shows both (S)-citalopram and paroxetine are docked with supporting electron
density. However, their docking positions are not completely unambiguous due to the resolution
limits of the diffraction data. The researchers used derivative compounds, like Br-citalopram, to
validate the placement of specific chemical groups within binding sites.
Lecture 28 – Amyloid Diseases and Antibodies
Common beta-pattern that form sheets, which then get “sandwiched” – tightly-packed, ordered
structures, specific characteristics – staining with dyes, etc.
We don’t see the whole protein – there is about 1/3 of the protein which is diffused and does not have
the orderly structure of the protein’s core
The protein alone isn’t that significant, also the interactions between proteins need to be studied –
amorphous aggregates can be formed, but also the amyloid structures
Different ways of protein packing
Over time, the structure shifts, and at a certain timepoint, something triggers the elongation phase 
there is an exponential growth, formation of aggregates will trigger more of the same
It is not the origin of the precursor protein that determines if amyloids will develop
At some point, it gets into a misfolded state, which then leads to disease
General stuff
-
Ideal hydrogen bond – 2.8 angstrom, cutoff is around 3.0-3.2
Charge-assisted hydrogen bond – one charged and one uncharged molecule, salt bridge –
two charged molecules
Amino acids:
Resolution  lower angstrom is better
Resolution Ranges:



Low resolution (>4.0 Å): Only the general shape and outline of the protein are visible,
but secondary structural elements are not easily discernible.
Medium resolution (2.5–4.0 Å): Some secondary structural elements, such as α-helices,
may start to become visible, but details are still limited.
High resolution (<2.5 Å): Clear visualization of α-helices and β-sheets becomes
possible, along with side chains and backbone details.
Ideal Resolution for Secondary Structure:

To confidently interpret secondary structures like α-helices and β-sheets, a resolution
of ~3.0 Å or better is typically needed. At this resolution, the overall fold of the protein
and the arrangement of secondary structures are evident.
Cryo-EM does NOT require crystallization (of the receptor) before data collection
Negative stain electron microscopy (EM) is used to enhance contrast in images of biological
specimens, especially when working with small, low-density samples – the resolution of negative
stain EM is limited to ~20 Å (2 nm) due to the grain size of the stain and other factors (so, much
lower than that of cryo-EM and X-ray)
α/β Proteins
- α-helices and β-sheets are interspersed and often arranged in alternating patterns
- The β-sheets are typically parallel and form a core structure, often surrounded by αhelices
-
Commonly found in fold motifs like the Rossmann fold or the TIM barrel (β-barrel core
surrounded by helices)
Often associated with enzymatic functions
The folding is more compact and interdependent
α+β Proteins
- α-helices and β-sheets are segregated into distinct regions of the protein, rather than
interspersed
- The β-sheets are often antiparallel
- Examples – lysozyme, hemoglobin
- The α-helices and β-sheets can form separate domains or layers.
- Structures are more modular compared to α/β proteins.
β-Sheet type
Folding
α/β Proteins
Interspersed α-helices and βsheets
Parallel
Compact and interdependent
Examples
TIM barrel, Flavodoxins
Feature
Arrangement
α+β Proteins
Segregated α-helices and βsheets
Antiparallel
Modular and separated
regions
Lysozyme, Hemoglobin
Domains of a protein
A peptidomimetic is a molecule that mimics the structure
and function of a peptide while offering improved stability,
bioavailability, or specificity. These molecules often replace
natural peptide bonds with non-peptidic elements to resist
enzymatic degradation or modify binding properties,
making them useful in drug design.
Kd = exp
(ΔG/RT)
All ion channels are membrane proteins – they are primarily made of transmembrane alpha
helixes; beta-sheets are common in extracellular domains
- Not all ion channels are involved in neurotransmission
- Not all ion channels are selective for a single species
- Not all ion channels hyperpolarize the membrane potential
- Not all ion channels contain an orthosteric binding site
- Not all ion channels depolarize the membrane potential
Donors: Count the number of -OH or -NH groups (hydrogen atoms attached to electronegative
atoms like oxygen or nitrogen).
- Hydroxyl (-OH), amine (-NH2, -NH), thiol (-SH)
Acceptors: Count the lone pairs on electronegative atoms (O or N, primarily).
Carboxyl oxygen (C=O), ether oxygen (-O-), hydroxyl oxygen (-OH, lone pairs), amine nitrogen
(with lone pairs, e.g., in -NH2)
Dopamine, (nor-)epinephrine – activate only GPCRs, no LGICs
Serotonin, acetylcholine (ACh), glutamate, GABA – activate both LGICs and GPCRs
Only exons are included in the mature messenger (mRNA) – 5’ UTR and 3’ UTR are parts of it
but do not code for an amino acid.
Kozak Sequence:
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The Kozak sequence is a conserved sequence in eukaryotic mRNAs that surrounds
the AUG start codon (the site where translation begins).
Its consensus sequence is typically (gcc)gccRccAUGG, where:
o R is a purine (adenine or guanine).
o AUG is the start codon.
It helps the ribosome recognize the start codon for translation initiation.
5' Cap (7-methylguanosine cap) → 5' UTR (contains regulatory sequences, including the Kozak
sequence) → Start Codon (AUG) → Coding Sequence (CDS) → Stop Codon → 3'
UTR → Poly-A Tail.
A pharmacophore is a conceptual model representing the essential features of a molecule that are
necessary for it to interact with a specific biological target (e.g., a protein or receptor) to produce
a desired biological effect – the minimum structural framework required.
Carboxylate ion
Nuclear receptor domains
Putative N-glycosylation sites  Asn-X-Ser/Thr, where:
- Asn (N): Asparagine is the site where the glycan is attached
- X: Any amino acid except proline (Pro  P), which disrupts the sequence
- Ser(S)/Thr (T): Serine or threonine, as hydroxyl-containing side chains are essential
- N-glycosylations have generally an N-acetylglucosamine attached directly to the
asparagine.
Nuclear localization sequence  short sequence (cluster) of positively charged amino acids –
mainly lysine (K) and arginine (R)  e.g., monopartite PKKKRKV in SV40 large T-antigen or
bipartite KRPAATKKAGQAKKKK in nucleoplasmin
Resolutions are NOT reported for structures determined with the Alphafold 2 program
Alphafold 2 does NOT rely solely on detection of co-evolution in multiple sequence alignments
siRNA are NOT transcribed from genomic DNA
Phosphorylation of a peptide – with a compound with 3 P atoms, happens at an OH
All enzyme inhibitors bind to an enzyme and block its activity, not all of them bind to the active
site, some can block K+ channels in the heart to create cardiac side effects
pKa of a phenolic -OH (hydroxyl group) = 10  at pH below 10, it will still be protonated
pKa of a secondary -NH (amine) = 9-11  at pH below 9-11, it will be protonated (NH2+)
Glutamic acid (Glu): -COOH chain  pKa = 4.1, NH2 group  pKa = 9.6
Aspartic acid (Asp): -COOH chain  pKa = 3.9, α-carboxyl group  pKa = 2.1, NH2 group 
pKa = 9.8
Histidine (His): imidazole nitrogen  pKa = 6.0
Lipophilic ligand efficiency (LLE): pIC50 – logP, where pIC50 = -log10(IC50)  IC50 needs to be
in M (e.g., 14 nM = 14*10^-9  pIC50 = -log10 (14*10^-9))
Isoforms of the encoded protein – because of genomic flexibility (alternative promoters, splice
sites, polyadenylation signals), RNA-level mechanisms (alternative splicing, RNA editing), or
post-translational modifications (modifying the protein's structure or function)
BLOSUM62 matrix
Enzyme inhibition – Kcat and Km
Competitive inhibition – the inhibitor competes with the substrate for binding to the enzyme's
active site
Non-competitive inhibition – the inhibitor binds to an allosteric site, not the active site, and can
bind whether the enzyme is free or substrate-bound
Uncompetitive inhibition – the inhibitor binds only to the enzyme-substrate complex, locking the
substrate in place and preventing product formation
ΔG = ΔH – TΔS (Gibbs free energy, enthalpy, entropy)
- ΔG: The overall free energy of binding (indicates binding affinity)  the lower the value,
the higher the binding affinity
- ΔH: Enthalpic contribution (reflects the strength of specific interactions like hydrogen
bonds, van der Waals forces, and electrostatic interactions)  the lower the value, the
stronger the specific favorable interactions
- TΔS: Entropic contribution (reflects changes in system disorder, like the release of water
molecules or flexibility of the protein-ligand complex)  less negative value is better for
binding because it means there is less entropy loss (disorder) when the ligand binds
A transmembrane helix must fully traverse the lipid bilayer, from one side (extracellular) to the
other (intracellular)
A compound with the best potential should have >100 for the most number subtypes of channels
Sense mutation – a single nucleotide substitution that doesn’t lead to a change in the amino acid
sequence
Missense mutation – a single nucleotide substitution that does lead to a change in the amino acid
sequence
BLAST doesn’t always necessarily compare entire full-length protein amino acid sequences
The BLAST bit score is a measure of the quality of the alignment between a query sequence and
a subject sequence (e.g., in a database). It reflects the alignment's statistical significance, with
higher scores indicating better alignments.
The observed Expect value (BLAST) reports the number of hits one can "expect" to see by
chance when searching the database and consequently suggests an evolutionary relationship
between the two proteins
The BLAST Expect Value (E-value) is a statistical measure that indicates the likelihood of
obtaining an alignment as good as (or better than) the one observed, purely by chance, in a
database of a given size. Essentially, it helps determine the statistical significance of the
alignment.
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Single particle cryoEM does require protein purification
X-ray crystallography does require protein purification
Using cryoEM, a density map of the protein can be derived directly from images of the
particles
Using X-ray, a density map of the protein CANNOT be derived directly from diffraction
images
In electron microscopy, staining of proteins with heavy atoms can also be undertaken
before imaging. This yields higher resolution than what can be achieved with cryoEM 
this is NOT TRUE
For both cryoEM and MX, density derivation is followed by fitting of an atomic model to
the density and a refinement of the structure
miRNA (micro RNA) vs mRNA:
Neurotransmitter transporters (NTTs):
- All mainly contain alpha helices in the transmembrane (TM) domain
- Not all are multimeric complexes formed by three subunits
- All contain binding sites for Na+ ions
- Not all contain binding sites for Ca2+ ions
- All span the lipid bilayer membrane
A cation-π (cation-pi) interaction is a non-covalent molecular interaction between a cation
(positively charged species) and the π-electron cloud of an aromatic system.
Inhibiting, e.g, a GABA transporter  increasing the effect of GABA
If a protein has three domains, it does not mean that it is a trimer – dimers, trimers, etc. are
quaternary structures, many polypeptide chains together and in different places; domains are
within a single polypeptide chain
Ramachandran plot – exam 2020
GPCRs, (anti-)histamine(s) – exam 2019 – start
Ligand-gated ion channels (LGICs):
- Do not have the same number of alpha helices in the transmembrane domain
- Are multimeric complexes
- Have an intracellular region (P2X channels do not have intracellular domains)
- Do not contain the same number of agonist binding pockets
- Contain a single ion channel pore in the transmembrane domain
- Span a lipid bilayer membrane
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Argiotoxin 636 inhibits nAChRs
Cyclothiazide inhibits AMPA receptors
Citalopram inhibits the reuptake of 5-HT (inhibits SERT)
Clomipramine inhibits SERT and NET
Picrotoxin inhibits GABA-a
Tiagabine inhibits the GABA transporter (GAT-1)
NMDA vs other ionotropic neurotransmitter receptors – question 9 from exam 2019
nAChRs and acetylcholine – question 10 from exam 2019
Polyamine toxins – target ionotropic glutamate (iGlu) receptors – question 2 from exam 2018
Locked nucleic acid – question 3 from exam 2018
Ionotropic glutamate (iGlu) receptors – question 4 from exam 2018
Statins – properties – question 5 from exam 2018
Glycine transporters (GlyTs) – question 6 from exam 2018
Glycine – NMDA receptors – schizophrenia – clomipramine, MK-801, ifenprodil, cyclothiazide,
NPTS, Mg2+ - question 7 from exam 2018
Glycine – glycine receptors (GlyR) – question 9 from exam 2018
All neurotransmitters and what amino acid they are derived from:
Disulphide bridges – formed between cysteine (C) residues, different combinations between
them
Most antibodies (antibody) – 12 domains (2 heavy chains x 4 domains + 2 light chains x 2
domains), 4 chains, antiparallel beta sheets (but check this last one with structure)
Ramachandran plot – question 15 from exam 2018
Peptides made by SPPS – question 1 from exam November 2017
Decreasing peptide clearance – adding a polymer (PEG) or a fatty acid chain to the peptide
Mechanisms of peptide clearance – question 4 from exam November 2017
Protein-based drugs – can be used to treat diabetes, cancer, and rheumatoid arthritis (RA), but
not CNS diseases (e.g., depression, Parkinson’s, anxiety)
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Cyclothiazide: Enhances AMPA receptor activity by preventing desensitization
Tiagabine: Inhibits GABA reuptake, increasing GABA availability in the synapse –
potential hypnotic
NMDA: Agonist for NMDA glutamate receptors, mimicking glutamate activity
Muscimol: Potent GABA-A receptor agonist, mimicking GABA's effects
Baclofen: GABA-B receptor agonist, reducing excitatory neurotransmission
Zolpidem: Selective GABA-A receptor agonist, promoting sedative and hypnotic effects
– potential hypnotic
Quaternary structure of the GABAA (GABA-A) receptor – question 7 from exam November
2017
Similarities of the GABA and glutamate neurotransmitter systems – question 10 from exam
November 2017
Antipsychotic drugs – primarily target he dopamine system (D2 receptors) and often the
serotonin system (5-HT2A receptors); also maybe the glutamate system (NMDA receptors)
If a sequence has RNA as substrate, it does not comprise a secretion signal peptide; signal
peptide – can be used to identify membrane bound and secreted proteins with
bioinformatics
Nuclear receptors – consists of a ligand-binding domain, DNA-binding domain, and a
transactivation domain
Kinases – protein kinases – kinase dependent catalysis – question 1 from exam January 2017
Technologies for introduction of site specific posttranslational modifications (PTMs) in peptides
and protein – question 2 from exam January 2017
Preventing enzymatic degradation of peptides/proteins (proteinogenic and nonproteinogenic
amino acids, lactam bridge, peptiods, N-methyl amino acids) – question 3 from exam January
2017
A peptoid is a synthetic polymer similar to a peptide, but its side chains are attached to the
nitrogen atom of the backbone instead of the alpha-carbon, making it resistant to enzymatic
degradation and giving it unique properties.
Proteinogenic Amino Acids: The 20 standard amino acids directly encoded by the genetic code
and used in ribosomal protein synthesis (e.g., alanine, lysine).
Non-Proteinogenic Amino Acids: Amino acids not directly encoded by the genetic code or used
in standard protein synthesis; they may be intermediates in metabolism (e.g., ornithine, citrulline)
or incorporated via post-translational modifications (e.g., hydroxyproline).
Functional nAChR – 20 alpha helices (4 in each of the 5 subunits) in the transmembrane domain
Natural products leading to death by targeting VGIC involved in control of muscle function –
argiotoxin, alpha-Bungarotoxin, capsaicin, tetrodotoxin, cocaine, muscarine – question 7 from
exam January 2017
Pore region of potassium channels – ion selectivity of voltage-gated potassium channels –
question 9 from exam January 2017