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Griffen MedChemica Virtual Tox Panel

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Bowes J., et al. Reducing safety-related drug attrition: the use of in vitro pharmacological profiling.
Nature Reviews Dru...

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Griffen MedChemica Virtual Tox Panel

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Virtual Toxicity panels focussed on interpretable machine learning models that can guide medicinal chemists to identify critical substructures that are assocaited with toxicities.

Virtual Toxicity panels focussed on interpretable machine learning models that can guide medicinal chemists to identify critical substructures that are assocaited with toxicities.

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Griffen MedChemica Virtual Tox Panel

  1. 1. Bowes J., et al. Reducing safety-related drug attrition: the use of in vitro pharmacological profiling. Nature Reviews Drug Discovery 2012;11:909–22. Number in training set 4585 3106 2457 Median difference with/without feature (DpIC50) 0.35 -0.1 0 Cohan’s d 0.4 -0.26 0.02 Explainable AI MedChemica Virtual Toxicity Panel Screens to aid the Medicinal Chemist A. G. Dossetter•, E. Griffen•, A. Leach•+, A. Lin‡, J. Stacey†, L. Reid§, S. Montague•. •Medchemica Ltd, Macclesfield, UK, + Pharmacy and Biomolecular Sciences, Liverpool John Moores University, †Information School, University of Sheffield, ‡Laboratory of Chemoinformatics, Faculty of Chemistry, University of Strasbourg, §Bioinformatics Institute (A*STAR), 30 Biopolis Street, Matrix, Singapore 138671 Problem Unforeseen toxicity via secondary pharmacology is a significant risk and when encountered late in a discovery project’s life creates major issues and may even terminate it. Chemists need to be alerted to potential risks but to be influenced they must be able to audit the reasons and evidence for the alerts. Solution Build transparent models of critical toxicity targets and communicate results in chemical structures rather than just numbers. This is an example of ‘Explainable AI’ for chemists contact@medchemica.com Learning • Models must be transparent and show structures to influence chemists • Random Forest models with the correct descriptors can be used to show important features as pharmacophores and the evidence supporting them • Error models can given a measure of confidence to predictions beyond use of an RMSE. Chemists won’t make decisions without understanding Language of medicinal chemists = structures / clear pharmacophores Machine Learning method Description MMPA transformations Example pairs kNN + Morgan fp Structures of Nearest Neighbours Random Forest + pharmacophore fp Compound highlighted with important features Graph analytics Connections between compound families Graph Convolutional Neural Network (GCNN) Graph node feature importance – a work in progress Aspects of Models Pay attention to Feature Engineering Clear definitions enables identifying key features Transparency Scientific Sense Consistency Parsimony Applicability Performance Modeler’s domain Chemist’s domain Interpretable Failure cost high Immature science Highly skilled, critical users Business-2-Business Transparent and auditable Black Box Failure cost is low Real time response critical Interactive = self correcting Business-2-consumer User agnostic of process Trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. - Cynthia Rudin Nature Machine Intelligence (2019), 206–215. Approach Application Advanced Pharmacophore Features Feature Definition Basic Group Atom or group most likely protonated at pH 7.4 Acidic Group Atom or group most likely deprotonated at pH 7.4, includes N and C acids Acceptor Definitions derived from Taylor & Cosgrove Donor Definitions derived from Taylor & Cosgrove Hydrophobic C4 or greater cyclic or acyclic alkyl group Aromatic Attachment connection of any group to an aromatic atom excluding connections within rings Aliphatic Attachment connection of any atom to an aliphatic group not in a ring. Halo F,Cl, Br, I Gobbi, A.; Poppinger, D. Biotechnology and Bioengineering 1998, 61 (1), 47–54. Reutlinger, M.; Koch, C. P.; Reker, D.; Todoroff, N.; Schneider, P.; Rodrigues, T.; Schneider, G. Mol. Inf. 2013, 32 (2), 133–138. Taylor, R.; Cole, J. C.; Cosgrove, D. A.; Gardiner, E. J.; Gillet, V. J.; Korb, O. J Comput Aided Mol Des 2012, 26 (4), 451–472. Acid & Base definitions are SMARTS including C, N, heteroaromatic acids, bases excluding weak aniline bases, including amidines, guanidine’s - MedChemica definitions. Simple H bond acceptor base acid Precise Diclofenac (1973) Sulfadiazine (1941) Pharmacophore Pairs • Feature 1 – topological distance - Feature 2 • Engineered for chemical relevance – pairs can be superimposed or directly linked, e.g. enables a group to be both a hydrogen bond acceptor and a base • Used as unfolded 280 bit fingerprints • A bit identifies a pharmacophore pair e.g. : Aromatic - 3 bonds - Base • Random Forest feature importance and Cohan’s d for effect size allow identification of critical features in models • Highlight atoms by S Feature Importance coloured by direction of Cohan’s d • Show statistics on the effect and variance of each feature • Drill back to precise features and original compounds with data supporting that feature – complete transparency Cardiac toxicity and Seizure are key toxicological risks Cardiac hERG ion channel inhibitor NaV 1.5 channel inhibitor Ca L type channel inhibitor Ca T-type channel inhibitor PDE 3A inhibitor Seizure Dopamine D1 receptor ant/ag Dopamine D2 receptor ant/ag Cannabinoid CB1 receptor ant/ag Acetylcholine a1b2 receptor agonist / antagonists µ opioid agonist / antagonists k opioid agonist d opioid agonist/ antagonists Muscarinic M1 receptor ant/ag Muscarinic M2 receptor ant/ag Seizure 5HT 1A receptor antagonists 5HT 1B receptor antagonists 5HT receptor antagonists 2A GABA a1 antagonist NMDA-NR1 agonist 5HT Transporter inhibitor Dopamine Transporter inhib Noradrenaline Transporter inh Acetylcholine esterase inhibitor Monoamine oxidase inhibitor PDE 4D inhibitor Model ‘quality’, Error models and Domain of applicability • Build models with 10 fold CV – report CV-Pearson’s R2 and CV RMSE • Build a Random Forest error model to generate predicted error for each compound • Error model can be used to flag compounds out of Domain of Applicability hERG n=5968, RMSE = 0.16, CV Pearson’s R2 = 0.27 CHEMBL12713 sertindole, prediction pIC50 7.8 [7.1 – 8.4], actual 8.2 . Predictions and Transparency Medicinal Chemistry Seizure Models – RF and kNN Dopamine Transporter Norepinephrine Transporter 5HT1a receptor GABA-A receptor d Opioid receptor MAO-A inhibitor AChE inhibitor Training set size 1712 1757 400 1526 1070 1684 3283 CV-R2 0.28 0.23 0.37 0.24 0.28 0.21 0.32 RMSE 0.13 0.18 0.21 0.29 0.18 0.29 0.16 Best Random Forest based models for seizure endpoints, All the seizure data sets delivered kNN models based on Morgan fingerprints hERG Example

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