The document discusses how drug discovery scientists could potentially be replaced by software systems in the future. It argues that drug discovery has become a mature field with established methodologies and best practices. It is presented as a multi-objective optimization problem that considers many potential drug targets, compounds, and goals. The document proposes that human understanding is no longer essential in drug discovery and that systems could select which compound to synthesize next through computational models and rules. It then provides examples of how expert strategies, workflows, and a "panel of experts" approach could be modeled computationally through packages, rules, and optimization engines to enable more automated "declarative drug design".

1. Most Drug Discovery Scientists could
be replaced by Software Systems
David E Leahy
Molplex

2. Propositions
• Discovery Logistics “ a done deal”
– Data and materials management processes built and running
• Discovery is Mature
– established domains, established methodologies
– best practice, strategies & success criteria
– Operational, engineering & incremental change
• Discovery is a multi-objective optimisation
– many genes, many (100’s) target, many drugs
– Human understanding is a nice to have, not essential
– Which compound do we make next?
• Discovery needs a Reboot
– Simplify, abstract & re-implement

3. Facts and Rules
100
Package “Metabolic Clearance”
90 rule “Last point outlier”
80
when
ObsVal.time(60) > FitVal.time(60) + 10
70
then
60
delete ObsVal.time(6)
50 refit
40
end
rule “another rule”
30
when
20
something == true
10 then
0
do something else
0 10 20 30 40 50 60 end

4. Facts, Events,Goals & Plans
Fact Package “Clearance”
Clearance(mol) = 50 ml/min rule “Predict clearance if no measurement”
Event Salience 10
when
add(mol) !getClearance(mol)
Goal then
Clearance(mol) = ? predictClearance(mol)
end
Sub-goals
getClearance(mol) rule “Important compound”
salience = 100
assayClearance(mol) when
predictClearance(mol) important(mol)
Plans then
assayClearance(mol)
sub-goal chains end

5. Sub-Goals and Plans
(predictClearance)
findModels(clearance)
testApplicationDomain(mol)
allModelPredict(mol)
consensusAverage(mol)
addClearance(mol)

6. Modelling Expert Strategies
Human Expert Systems
• Best Practice • Best Practice
– How – Workflows
• Tacit Knowledge • Tacit Knowledge
– When – Rules (facts, events)
– Which – Competitive workflow
• Quality • Quality
– Success criteria – Panel of experts

7. Competitive Workflow for QSAR
removeTest selectSeries calcDescriptors filterFeatures buildModel predict
•Random •cluster •CDK •Stats •Linear •Ensemble
•ordered •scaffold •CDL •GA •NN •Weighted
•HState … •best

8. QSAR Panel of Experts

9. Testing the Expert QSAR System
CHEMBL Database: data on 622,824 compounds,
collected from 33,956 publications
WOMBAT Database: data on 251,560 structures,
for over 1,966 targets
WOMBAT-PK Database: data on 1230 compounds,
for over 13,000 clinical measurements
Project Junior (Newcastle University & Microsoft Research)
10,000 datasets gave 750,000 QSAR models in 3 weeks using 100 Azure Cloud
Servers
From 750,000 QSAR models, 3,000 were judged stable and valid

10. QSAR Models

11. Panel of Experts

12. Events & Dashboards
Event Rule Set Workflow Fact
• Add(data) • What • Goal chain • New facts
• Add(mol) strategy? • workflow • New events
• Add(reaction) • Competitive • New rules
• Add(reagent) workflow
• Add(goal)

13. Declarative Drug Design
• Target Product Profile Package “TPP”
rule “potency”
– Panel of experts for a project when
– Set of rules potency(mol)==high
– Sub-Target profiles then
(hit, lead, candidate) addLeads(mol)
• Goals end
rule “ good ADME”
– Query when
– Assay solubility(mol) > $minSol &&
– predict Papp(mol)> $minPapp && …
• Engines then
addLeads(mol)
– Forward chaining
– Backward chaining rule “no Tox”
– Workflow when
– Competitive workflow someToxEndPoint < someVal
then
– Multi-Objective Optimisation addLeads(mol)
end

14. Multi-Property Optimisation Engines

15. Reboot