Most Drug Discovery Scientists could be replaced by Software Systems

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Drug discovery is a mature field of applied of scientific methods with well understood strategies, decisions and processes captured mainly as organisational tacit knowledge. The talk argues that this tacit knowledge can be managed by sofwtare systems which have the potential to transform the quality and scalability of drug discovery.

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Most Drug Discovery Scientists could be replaced by Software Systems

David E LeahyMolplex

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

Facts and RulesPackage “Metabolic Clearance”rule “Last point outlier” when ObsVal.time(60) > FitVal.time(60) + 10thendelete ObsVal.time(6)refitend rule “another rule” whensomething == truethendo something elseend 0 10 20 30 40 50 60

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Facts, Events,Goals & PlansFact

Clearance(mol) = 50 ml/minEvent

add(mol)Goal

Clearance(mol) = ?Sub-goals

getClearance(mol)assayClearance(mol)predictClearance(mol)

Planssub-goal chains

Package “Clearance”

rule “Predict clearance if no measurement”Salience 10when

!getClearance(mol)then

predictClearance(mol)end

rule “Important compound”salience = 100when

important(mol)then

assayClearance(mol)end

findModels(clearance)

testApplicationDomain(mol)

allModelPredict(mol)

consensusAverage(mol)

addClearance(mol)

Sub-Goals and Plans(predictClearance)

Modelling Expert Strategies

Human Expert• Best Practice

– How

• Tacit Knowledge– When– Which

• Quality– Success criteria

Systems• Best Practice

– Workflows

• Tacit Knowledge– Rules (facts, events)– Competitive workflow

• Quality– Panel of experts

Competitive Workflow for QSAR

removeTest

•Random•ordered

selectSeries

•cluster•scaffold

calcDescriptors

•CDK•CDL•HState …

filterFeatures

•Stats•GA

buildModel

•Linear•NN

predict

•Ensemble•Weighted•best

QSAR Panel of Experts

Testing the Expert QSAR System

CHEMBL Database: data on 622,824 compounds,collected from 33,956 publications

WOMBAT-PK Database: data on 1230 compounds,for over 13,000 clinical measurements

WOMBAT Database: data on 251,560 structures,for over 1,966 targets

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

QSAR Models

Panel of Experts

Events & Dashboards

Event

• Add(data)• Add(mol)• Add(reaction)• Add(reagent)• Add(goal)

Rule Set

• What strategy?

Workflow

• Goal chain• workflow• Competitive

workflow

Fact

• New facts• New events• New rules

Declarative Drug Design• Target Product Profile

– Panel of experts for a project– Set of rules– Sub-Target profiles (hit, lead,

candidate)• Goals

– Query – Assay– predict

• Engines– Forward chaining– Backward chaining– Workflow– Competitive workflow– Multi-Objective Optimisation

Package “TPP”rule “potency”when

potency(mol)==highthen

addLeads(mol)endrule “ good ADME”when

solubility(mol) > $minSol &&Papp(mol)> $minPapp && …

thenaddLeads(mol)

rule “no Tox”when

someToxEndPoint < someValthen

addLeads(mol)end

Multi-Property Optimisation Engines

Reboot

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