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

  1. 1. Most Drug Discovery Scientists could be replaced by Software Systems David E Leahy Molplex
  2. 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. 3. Facts and Rules100 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. 4. Facts, Events,Goals & PlansFact 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) endSub-goals getClearance(mol) rule “Important compound” salience = 100 assayClearance(mol) when predictClearance(mol) important(mol)Plans then assayClearance(mol) sub-goal chains end
  5. 5. Sub-Goals and Plans (predictClearance)findModels(clearance) testApplicationDomain(mol) allModelPredict(mol) consensusAverage(mol) addClearance(mol)
  6. 6. Modelling Expert StrategiesHuman 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. 7. Competitive Workflow for QSARremoveTest selectSeries calcDescriptors filterFeatures buildModel predict •Random •cluster •CDK •Stats •Linear •Ensemble •ordered •scaffold •CDL •GA •NN •Weighted •HState … •best
  8. 8. QSAR Panel of Experts
  9. 9. Testing the Expert QSAR SystemCHEMBL Database: data on 622,824 compounds, collected from 33,956 publicationsWOMBAT Database: data on 251,560 structures, for over 1,966 targetsWOMBAT-PK Database: data on 1230 compounds, for over 13,000 clinical measurementsProject Junior (Newcastle University & Microsoft Research)10,000 datasets gave 750,000 QSAR models in 3 weeks using 100 Azure CloudServersFrom 750,000 QSAR models, 3,000 were judged stable and valid
  10. 10. QSAR Models
  11. 11. Panel of Experts
  12. 12. Events & DashboardsEvent 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. 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. 14. Multi-Property Optimisation Engines
  15. 15. Reboot

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