The Silicon Chemist: Using logic-based machine learning
and virtual chemistry to design new drugs automatically.
Christopher Reynolds and Mike Sternberg
Predicted pKa
Observed
activity
New drug leads are always needed, and
virtual screening is now often used to simulate
the bioactivity of compounds, in order to search
as much of chemical space as possible in a
reasonable amount of time. This project takes an
existing logic based machine-learning approach
to identifying bioactive compounds, and
incorporates chemical synthesis rules, to design
novel, easily synthesisable, and effective
pharmaceutical drugs.
True positives
True negatives
False negatives
Fragmentation
of molecules
Figure 3. A scatter
plot showing observed
vs. predicted pKa in a
cross validation of a
PubChem
bioassay.
This shows that there
are no false positives
when using an activity
cut-off of pKa 7.
Top 1% (6 actives)
Top 5% (25 actives)
Top 10% (47 actives)
Active/Inactive
(232 actives)
Observed pKa
active(A):- positive(A, B), Nsp2(A, C),
distance(A, B, C, 2.49, 0.5).
Logic-based drug discovery
• Inductive Logic Programming (ILP) is a
machine learning technique, which learns human
interpretable qualitative rules from chemical
knowledge of active drugs (see Figure 2), which
relate structure to activity, and can guide the next
steps drug design chemistry.
• Using Partial Least-Squares or Support Vector
Machines, the rules can then be weighted
• Weighted rules used as a quantitative model
of Quantitative Structure-Activity Relationship
(QSAR) to predict drug activity.
• This
approach
combines
the
human
comprehensible rules of ILP with the predictive
accuracy of advanced regression techniques.
• This
Support
Vector
Inductive
Logic
Programming (SVILP) method is being patented.
• INDDEx (Investigational Novel Drug Discovery
by Example) is a proprietary virtual screening
program, incorporating SVILP, and developed by
Equinox Pharma Ltd., an Imperial College spinoff company.
• The QSAR model is then used to screen a
database of all purchasable molecules to identify
drug leads.
• In a blind test, INDDEx had a hit rate of 30%,
predicting around 30 active molecules, each
capable of being the start of a new drug series,
and each sufficiently novel that it could be
patented.
False positives
Department of Computing, Imperial
College, and Department of Life Sciences,
Imperial College
True positive rate
Introduction
Results
Database
of virtual
reactions
Molecule is active if there is
positive charge centre and an sp2
nitrogen atom 2.49±0.5Å. apart
SVILP
generates
QSAR
rules
active(A):- phenyl(A, B), phenyl(A, C),
distance(A, B, C, 4.1, 0.5).
Molecule is active if there are two
phenyl rings 4.1±0.5Å apart.
Figure 2. Example ILP QSAR
rules.
Modified
molecules
False positive rate
Figure 4. Receiver Operating Characteristic (ROC)
curve, showing fraction of true positives against true
negatives retrieved as the discrimination threshold is
varied. ROC values for 1%, 5%, 10% and all actives
are 0.912, 0.830, 0.814, and 0.892 respectively. This
illustrates the sensitivity of active compound detection
to hold true up to the top 5% most
active compounds provided to
INDDEx as examples.
Screen
Molecular
database
Screen
Split molecule
with reverse
reaction
Hit rate
30%
Novel
verified hits
Modify using
all viable
reactions
Novel verified hits
on synthesisable
molecules
Figure 5. From left to right:
wireframe visualisations of the two
Hit rate reactant molecules entered in
?% SMILES format are processed by
the
SMIRKS
esterification
reaction, and the resultant product
molecule is returned.
Virtual chemistry
To extend the machine learning method currently used in
INDDEx, and move the process from hit to lead discovery, the
Figure 1. Graphic showing the current method of
logic-based rules produced by SVILP will be used to modify
finding matches for ILP derived rules, as used by
promising hits. This new process is shown in Figure 1. This
the INDDEx software, and this project’s new
project will concentrate on scanning through the dataset of
method.
purchasable molecules that partially fulfil the rules, and then
altering the molecules to try and fit the remaining rules using a
database of virtual chemical synthesis reactions to combine these molecules with a database of fragment-like molecules.
Through this method, the program will generate focussed libraries of synthetic derivatives around the promising hit
molecules, and so explore a far greater section of easily synthetically accessible chemical space.