TABLE 1 Our selection of 99 JCAMD articles that describe the expectations and outcomes of
the hypes in rational drug design.
Our selection of 99 JCAMD articles that describe the expectations and outcomes of the hypes in rational drug Ref.
design.
CONFORMATIONAL ANALYSIS:
(1) Conformation-activity relationships of opiate analgesics (molecular mechanics, 1987);
(2) A unique geometry of the active site of angiotensin-converting enzyme consistent with
structure-activity studies (conformational hyperspace search, 1987);
(3) Conformational studies on (+)-anatoxin-a and derivatives (semi empirical, 1992);
(4) QSAR and conformational analysis of the antiinflammatory agent amfenac and analogues
(semi-empirical, 1993);
(5) A fast new approach to pharmacophore mapping and its application to dopaminergic and
benzodiazepine agonists (low-energy/bio-active conformations generation, 1993);
(6) SCF-MO study of the polyglycine II structure (semi-empirical, 1994);
(7) Conformational studies on the four stereoisomers of the novel anticholinergic 4(dimethylamino)-2-phenyl-2-(2-pyridyl)pentanamide (semi-empirical, 1996));
(8) Comparison of conformer distributions in the crystalline state with conformational energies
calculated by ab initio techniques (ab initio, 1997);
(9) Do active site conformations of small ligands correspond to low free-energy solution
structures? (bio-active conformer, 1999);
(10) Molden: a pre- and post-processing program for molecular and electronic structures*(semiempirical, ab initio, 2000);
(11) Can we separate active from inactive conformations?(bio-active conformer, 2003);
[39]
[38]
[15]
[16]
MOLECULAR DYNAMICS SIMULATION:
(12) Thermodynamic cycle integration by computer simulation as a tool for obtaining free energy
differences in molecular chemistry (free energy calculations,1987);
(13) Symposium overview Minnesota Conference on Supercomputing in Biology: Proteins, Nucleic
Acids, and Water(protein dynamics simulation, 1987);
(14) Future in biomolecular computation(large biomolecular assembly simulation, 1987);
(15) Ligand binding affinity prediction by linear interaction energy methods(ligand binding affinity
calculations, 1998);
(16) Modeling of enzyme–substrate complexes for the metalloproteases MMP-3, ADAM-9 and
ADAM-10 (substrate-specificity, 2004);
(17) Solvent effect on the synthesis of clarithromycin: A molecular dynamics study(solvent effect,
2005);
(18) Enhancing the accuracy of virtual screening: molecular dynamics with quantum-refined force
fields(docking by MDS, improved electrostatics,2005;
(19) Protonation-induced stereoisomerism in nicotine: Conformational studies using classical
(AMBER) and ab initio (Car–Parrinello) molecular dynamics(conformational study, 2006);
(20) Dynamical Aspects of TEM-1 β-Lactamase Probed by Molecular Dynamics(equilibrium
conformation in solution, 2006);
(21) Can free energy calculations be fast and accurate at the same time? Binding of low-affinity,
non-peptide inhibitors to the SH2 domain of the src protein (free energy estimates, 2006);
(22) Inactive and active states and supramolecular organization of GPCRs: insights from
computational modeling(activation mechanism, 2007);
(23) Molecular dynamics simulation study of PTP1B with allosteric inhibitor and its application in
receptor based pharmacophore modeling (conformational space, 2008);
(24) What induces pocket openings on protein surface patches involved in protein–protein
interactions?(solvent influence, conformational space, 2009);
[29]
[40]
[17]
[18]
[35]
[42]
[19]
[45]
[31]
[32]
[30]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
DOCKING:
Scoring functions
(25) The development of a simple empirical scoring function to estimate the binding constant for
a protein-ligand complex of known three-dimensional structure(empirically derived freeenergy scoring, 1994);
(26) Evaluating docked complexes with the HINT exponential function and empirical atomic
hydrophobicities(hydrophobicity scoring function, 1994);
(27) Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned
to compute binding affinities(entropic and solvation effects, 1996);
(28) Further development and validation of empirical scoring functions for structure-based
binding affinity prediction(deformation penalty, hydrophobic effect, 2002);
(29) A new method for estimating the importance of hydrogen-bonding groups in the binding site
of a protein (scoring of hydrogen-bond accessibility, 2003);
(30) A novel scoring function for molecular docking (self-consistent scoring, 2003);
(31) Validation of an empirical RNA-ligand scoring function for fast flexible docking using
RiboDock®( );
(32) Scoring Functions – the First 100 Years(review, 2005);
(33) Protein Alpha Shape (PAS) Dock: A new gaussian-based score function suitable for docking in
homology modelled protein structures(RNA-ligand scoring function, 2006);
(34) Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and
knowledge-based search(knowledge-based scoring, 2007);
(35) An improved scoring function for suboptimal polar ligand complexes(scoring better the weak
binders, 2009);
(36) Docking flexible ligands in proteins with a solvent exposure- and distance-dependent
dielectric function(dehydration energy, 2010);
(37) Transferable scoring function based on semiempirical quantum mechanical PM6-DH2
method: CDK2 with 15 structurally diverse inhibitors(semi-empirical scoring function, 2011);
[52]
[53]
[54]
[55]
[56]
[57]
[58]
[59]
[63]
[60]
[61]
[77]
[62]
[64]
Ligand flexibility
(38) FLOG: A system to select ‘quasi-flexible’ ligands complementary to a receptor of known
three-dimensional structure(clique finding algorithm, conformer library, 1994);
(39) Flexibases: A way to enhance the use of molecular docking methods (conformer database,
1994);
(40) Flexible ligand docking using a genetic algorithm(genetic algorithm, 1995);
(41) Comparison of two implementations of the incremental construction algorithm in flexible
docking of thrombin inhibitors(DOCK, FlexX, 1999);
(42) DREAM++: Flexible docking program for virtual combinatorial libraries(hybrid backtrack and
incremental ligand construction, 1999);
(43) DOCK 4.0: Search strategies for automated molecular docking of flexible molecule
databases(incremental ligand construction and random search, 2001);
(44) Flexible docking under pharmacophore type constraints(pharmacophore-guided docking,
2002);
(45) On the molecular interaction between lactoferrin and the dye Red HE-3B. A novel approach
for docking a charged and highly flexible molecule to protein surfaces(docking on the protein
surface, 2002);
(46) Comparative study of several algorithms for flexible ligand docking(DOCK, FlexX, GOLD
AutoDock, IMC, 2003);
(47) Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and
knowledge-based search(knowledge-based guided docking, 2007)
[65]
[66]
[67]
[68]
[69]
[70]
[71]
[72]
[60]
[73]
Receptor flexibility
(48) A new method for ligand docking to flexible receptors by dual alanine scanning and
refinement (SCARE)(induced fit docking, 2008);
(49) Protein–ligand docking with multiple flexible side chains( FlipDock, cross docking method,
2008);
(50) An improved adaptive genetic algorithm for protein–ligand docking(adaptive genetic
[74]
[75]
[76]
algorithm, 2009);
(51) Effects of protein conformation in docking: improved pose prediction through protein pocket [77]
adaptation(protein conformation from multiple complexes, 2009);
(52) Docking flexible ligands in proteins with a solvent exposure- and distance-dependent
dielectric function(Monte Carlo search, 2010);
[100]
CoMFA:
(53) Experimental design based 3-D QSAR analysis of steroid-protein interactions: Application to
human CBG complexes(comparing PCA and PLS analysis, 1990);
(54) Comparative molecular field analysis and energy interaction studies of thrombin-inhibitor
complexes(ab initio, hydrophobic field, 1999);
(55) QSAR of conformationally flexible molecules: Comparative Molecular Field Analysis of
protein-tyrosine kinase inhibitors(addressing conformationally flexibility, 2002);
(56) Comparative molecular field analysis of CCK-A antagonists using field-fit as an alignment
technique. A convenient guide to design new CCK-A ligands(field-fit alignment, 2002);
(57) CoMFA validation of the superposition of six classes of compounds which block GABA
receptors non-competitively(improved alignment across ligand classes, 2003);
(58) A new procedure for improving the predictiveness of CoMFA models and its application to a
set of dihydrofolate reductase inhibitors (improved alignment method, 2005);
(59) 3D-QSAR study of hallucinogenic phenylalkylamines by using CoMFA approach(comparing
ligand conformation, 2007);
[101]
[104]
[105]
[106]
[107]
[102]
[128]
HOMOLOGY MODELLING:
(60) Molecular modelling studies on the ORL1-receptor and ORL1-agonists(combined approach,
2003);
(61) Homology modeling, force field design, and free energy simulation studies to optimize the
activities of histone deacetylase inhibitors(combined approach success, optimizing inhibitor
potency, 2004);
(62) Homology modeling and molecular interaction field studies of α-glucosidases as a guide to
structure-based design of novel proposed anti-HIV inhibitors(combined approach success,
novel inhibitors design, 2005);
(63) Understanding and modulating cyclin-dependent kinase inhibitor specificity: molecular
modeling and biochemical evaluation of pyrazolopyrimidinones as CDK2/cyclin A and
CDK4/cyclin D1 inhibitors( combined approach success, improving inhibitor potency, 2005);
(64) Comparison of a homology model and the crystallographic structure of human 11βhydroxysteroid dehydrogenase type 1 (11βHSD1) in a structure-based identification of
inhibitors(homology modelling, virtual screening, 2006);
(65) Generation of a homology model of the human histamine H 3 receptor for ligand docking and
pharmacophore-based screening(combined approach success, identification of active hits,
2007);
(66) Discovery of small molecule inhibitors of ubiquitin-like poxvirus proteinase I7L using
homology modeling and covalent docking approaches(combined approach success,
identification of leads, 2007);
(67) Ligand-guided optimization of CXCR4 homology models for virtual screening using a multiple
chemotype approach(virtual screening, 2010);
(68) Structure-guided fragment-based in silico drug design of dengue protease inhibitors(
combined approach success, hit identification, 2011);
(69) In silico identification of new ligands for GPR17: a promising therapeutic target for
neurodegenerative diseases(combined approach success, partial agonist identification,
2011);
[111]
[112]
[113]
[129]
[114]
[115]
[116]
[117]
[118]
[139]
DE NOVO DESIGN
(70) The computer program LUDI: A new method for the de novo design of enzyme
inhibitors(placement of fragments to satisfy H-bond and hydrophobic interaction, 1992);
[140]
(71) LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads(exploring
the branchment of new substituents, 1992);
(72) GenStar: A method for de novo drug design(steric contact optimization and low-energy
conformer, 1993);
(73) Automated molecular design: A new fragment-joining algorithm(synthesizable
pharmacophore-matching fragment linking, 1994);
(74) PRO_LIGAND: An approach to de novo molecular design. 1. Application to the design of
organic molecules(graph-theoretical algorithm for fragment placing, 1996);
(75) BUILDER v.2: Improving the chemistry of a de novo design strategy(improving the diversity of
bridging fragments, 1996);
(76) The atom assignment problem in automated de novo drug design. 1. Transferability of
molecular fragment properties (transferability of fragment electrostatic potential, 1996);
(77) Flexible ligand docking using a genetic algorithm(genetic algorithm to explore ligand
conformations and orientations; 1996);
(78) PRO_LIGAND: An approach to de novo molecular design. 3. A genetic algorithm for structure
refinement(genetic algorithm added in PRO LIGAND, 1996);
(79) Placement of medium-sized molecular fragments into active sites of proteins(pattern
recognition for fragment placement, 1997);
(80) De novo design of molecular architectures by evolutionary assembly of drug-derived building
blocks(evolutionary assembly of fragments, 2000);
(81) The de novo design of median molecules within a property range of interest(mean molecular
polarisability and aqueous solubility , 2004);
(82) ENPDA: an evolutionary structure-based de novo peptide design algorithm(evolutionary
method, successful design of inhibitors,2005)
(83) Second-generation de novo design: a view from a medicinal chemist
perspective(retrospective evaluation work, 2009);
[145]
[147]
[141]
[146]
[144]
[66]
[142]
[143]
[150]
[132]
[133]
[134]
[153]
VIRTUAL SCREENING
(84) An approach to computer-aided inhibitor design: Application to cathepsin L(successful
identification of active hits, 1992);
(85) Combinatorial docking and combinatorial chemistry: Design of potent non-peptide thrombin
inhibitors(successful hit identification in nM range, 1999);
(86) Identification of ligands for RNA targets via structure-based virtual screening: HIV-1
TAR(successful hit identification, 2000);
(87) Identification of cylin-dependent kinase 1 inhibitors of a new chemical type by structurebased design and database searching(successful identification of active analogues, 2001);
(88) Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on
CCR5 binding affinity using QSAR and virtual screening techniques(successful identification of
analogues with improved activity, 2006);
(89) Development and application of hybrid structure based method for efficient screening of
ligands binding to G-protein coupled receptors(successful example, 2006);
(90) A successful virtual screening application: prediction of anticonvulsant activity in MES test of
widely used pharmaceutical and food preservatives methylparaben and
propylparaben(successful example, 2007);
(91) Community benchmarks for virtual screening (evaluation of mistakes, 2008);
(92) Comparative virtual screening and novelty detection for NMDA-GlycineB
antagonists(successful iidentification of novel compound classes, 2009);
(93) Trainable structure–activity relationship model for virtual screening of CYP3A4
inhibition(drug-drug interaction, ADME/Tox., 2010).
[154]
[155]
[156]
[157]
[158]
[159]
[160]
[161]
[162]
[174]
PATTERN RECOGNITION/NEURAL NETWORK
(94) Quantitative structure-activity relationships by neural networks and inductive logic
programming. II. The inhibition of dihydrofolate reductase by triazines(cross validation of
neural network; ’In this study, the methods performed surprisingly poorly compared to what [170]
might have been expected from the literature’[sic],1994);
(95) Compass: A shape-based machine learning tool for drug design (automatic ligand
conformation and alignment selection, 1994);
(96) Data modelling with neural networks: Advantages and limitations (neural network
limitations, 1997);
(97) Steroid binding by antibodies and artificial receptors: Exploration of theoretical methods to
determine the origins of binding affinities and specificities (Coordination affinity revisited,
2000);
(98) Molecular docking studies on tetrahydroimidazo-[4,5,1-jk][1,4]-benzodiazepinone (TIBO);
derivatives as HIV-1 NNRT inhibitors (novel scaffold determination, 2008);
(99) Machine learning study for the prediction of transdermal peptide(peptide sequence
information, 2011);
[175]
[169]
[173]
[172]