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Machine Learning / AI in Drug Discovery
Medicinal & Synthetic Chemistry Perspective Dr Ed Griffen Technical Director MedChemica
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Machine Learning / AI in Drug Discovery
Medicinal & Synthetic Chemistry Perspective TI LG LO Organization Scientist Data and Technology Ed Griffen 2018
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ML in Chemistry Maturity > than any chemist & database
= an experienced chemist with a large Pharma database = experienced chemist = graduate chemist Random – no effect
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Synthesis route design
ML in Chemistry Maturity $ Value Virtual Screening Synthesis route design Potency Optimization ADME Optimization Tox Alerts Polymorph Prediction LG LO Ed Griffen 2018
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Open source and cloud driving the revolution
Data and Technology Open source and cloud driving the revolution Cloud zero barrier to entry instantly scalable massive user base Data DB technologies ubiquitous Public data growing Large volumes of well curated data essential Machine Learning Libraries ubiquitous Large user base Ed Griffen 2018
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Only Big Pharma have enough data?
Data and Technology Only Big Pharma have enough data? NLP – Natural Language Processing BenevolentAI Ed Griffen 2018
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Synthesis route design
ML in Chemistry Maturity Virtual Screening ADME Optimization Synthesis route design Polymorph Prediction Tox Alerts Potency Optimization $ Value Ed Griffen 2018
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Better Project decisions Increased Medicinal Chemistry learning
ADME Optimization Making a real textbook of Medicinal Chemistry MMPA Combine and Extract Rules AstraZeneca, Roche and Genentech ADMET data > rules Better Project decisions Increased Medicinal Chemistry learning Learning Medicinal Chemistry Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Rules from Cross-Company Matched Molecular Pairs Analysis (MMPA). Kramer, Robb, Ting, Zheng, Griffen, et al. J. Med. Chem. 2017
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Synthesis Route Design
Automated Retrosynthetic analysis comes of age
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Help the HiPPOs – or they’ll crush you
Scientists Help the HiPPOs – or they’ll crush you “Companies often make most of their important decisions by relying on “HiPPO”—the highest-paid person’s opinion.”1 Chemistry HiPPs: experts in pattern recognition judged on their ability to make the best decisions with partial data highly trained time poor delivery focused gatekeepers to the adoption of new approaches McAfee & Brynjolfsson “Big Data: The Management Revolution”, Harvard Business Review October 2012 Ed Griffen 2018
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Is ML in chemistry mature enough yet?
Scientists Is ML in chemistry mature enough yet? Replace the chemist with automation? Propose Compounds Advantages Speed Lack of bias Cost per compound Scalability No explanations needed! Risks Fragile models Unclear where they fail Un-auditable MedChemica Prediction & error prediction Sort &Filter Like a blind man in a Tesla on Auto pilot… what could go wrong [Al Pacino as Frank Slade in ‘Scent of a woman”] [do you want your project manager to be like Chris O’Donnell?] Lee Cronin Nature (559) , 377 – 381 (2018)
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Scientists Christian Tyrchan AZ Gothenberg Ed Griffen 2018
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Chemists enhanced by Computational Intelligence
Scientists Chemists enhanced by Computational Intelligence Advantages Integrating experience Critical analysis Stopping ‘stupid’ errors Propose Compounds MedChemica context Prediction & error prediction Sort &Filter Risks Complexity Slower: humans in decision loop Critical: Person – machine interface Lee Cronin Nature (559) , 377 – 381 (2018)
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Scientists Ed Griffen 2018
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Organization Machine Learning challenges (powerful) cognitive workers
Technologies cross previous organizational boundaries Options to address internal organizational resistance to change: Skunkworks Acquisitions Ed Griffen 2018
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Machine Learning / AI in Drug Discovery
Medicinal & Synthetic Chemistry Perspective Summary AI is delivering on the back of massive data sets, turn-key high performance computing ubiquitous machine learning libraries choose: build it or buy it Challenges Knowledge workers are more challenged by AI Integration or automation Organizational change Ed Griffen 2018
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