Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis

Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field.

[1]  Kevin R. Harwell.  United States Patent and Trademark Office , 2002 .

[2]  Michael A. Galella,et al.  Discovery of Branebrutinib (BMS-986195): A Strategy for Identifying a Highly Potent and Selective Covalent Inhibitor Providing Rapid in Vivo Inactivation of Bruton's Tyrosine Kinase (BTK). , 2019, Journal of medicinal chemistry.

[3]  Bartosz A Grzybowski,et al.  Synergy Between Expert and Machine-Learning Approaches Allows for Improved Retrosynthetic Planning. , 2019, Angewandte Chemie.

[4]  G. V. Paolini,et al.  Quantifying the chemical beauty of drugs. , 2012, Nature chemistry.

[5]  Connor W. Coley,et al.  SCScore: Synthetic Complexity Learned from a Reaction Corpus , 2018, J. Chem. Inf. Model..

[6]  Charles C. Persinger,et al.  How to improve R&D productivity: the pharmaceutical industry's grand challenge , 2010, Nature Reviews Drug Discovery.

[7]  Allan M Jordan,et al.  The medicinal chemist's toolbox: an analysis of reactions used in the pursuit of drug candidates. , 2011, Journal of medicinal chemistry.

[8]  Akihiro Kishimoto,et al.  Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning , 2019, NeurIPS.

[9]  V. Caron,et al.  United states. , 2018, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[10]  J. Reymond,et al.  ‘Ring Breaker’: Assessing Synthetic Accessibility of the Ring System Chemical Space , 2019 .

[11]  Constantine Bekas,et al.  Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation , 2018, ArXiv.

[12]  Connor W. Coley,et al.  Autonomous discovery in the chemical sciences part II: Outlook , 2020, Angewandte Chemie.

[13]  Matthew H Todd,et al.  Computer-aided organic synthesis. , 2005, Chemical Society reviews.

[14]  Yuedong Yang,et al.  Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks , 2019, ArXiv.

[15]  R. W. Hansen,et al.  Journal of Health Economics , 2016 .

[16]  Connor W. Coley,et al.  Machine Learning in Computer-Aided Synthesis Planning. , 2018, Accounts of chemical research.

[17]  Kevin Bateman,et al.  Nanomole-scale high-throughput chemistry for the synthesis of complex molecules , 2015, Science.

[18]  Mark A. Murcko.  Envisioning the Future: Medicine in the Year 2050 , 2012 .

[19]  Paul Richardson,et al.  A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow , 2018, Science.

[20]  Michael G. Hutchings,et al.  Route Design in the 21st Century: The ICSYNTH Software Tool as an Idea Generator for Synthesis Prediction , 2015 .

[21]  Allan M Jordan,et al.  Artificial Intelligence in Drug Design-The Storm Before the Calm? , 2018, ACS medicinal chemistry letters.

[22]  E J Corey,et al.  Computer-assisted design of complex organic syntheses. , 1969, Science.

[23]  Richard N. Zare,et al.  Optimizing Chemical Reactions with Deep Reinforcement Learning , 2017, ACS central science.

[24]  Piotr Dittwald,et al.  Computer-Assisted Synthetic Planning: The End of the Beginning. , 2016, Angewandte Chemie.

[25]  Markus Reiher,et al.  Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes. , 2018, Journal of chemical theory and computation.

[26]  Le Song,et al.  Retrosynthesis Prediction with Conditional Graph Logic Network , 2020, NeurIPS.

[27]  Robert P. Sheridan,et al.  Modeling a Crowdsourced Definition of Molecular Complexity , 2014, J. Chem. Inf. Model..

[28]  Piotr Dittwald,et al.  Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory , 2018 .

[29]  V. Batista,et al.  Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists. , 2019, Chemical reviews.

[30]  Fabrice Gallou,et al.  Surfactant technology applied toward an active pharmaceutical ingredient: more than a simple green chemistry advance , 2016 .

[31]  M. Parmentier,et al.  A General and Practical Alternative to Polar Aprotic Solvents Exemplified on an Amide Bond Formation , 2016 .

[32]  Yurii S. Moroz,et al.  Ultra-large library docking for discovering new chemotypes , 2019, Nature.

[33]  Juno Nam,et al.  Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions , 2016, ArXiv.

[34]  Marwin H. S. Segler,et al.  Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction. , 2017, Chemistry.

[35]  Pieter P. Plehiers,et al.  A robotic platform for flow synthesis of organic compounds informed by AI planning , 2019, Science.

[36]  Daniel M. Lowe,et al.  Development of a Novel Fingerprint for Chemical Reactions and Its Application to Large-Scale Reaction Classification and Similarity , 2015, J. Chem. Inf. Model..

[37]  Alán Aspuru-Guzik,et al.  Neural Networks for the Prediction of Organic Chemistry Reactions , 2016, ACS central science.

[38]  Jibo Wang,et al.  Context Aware Data-Driven Retrosynthetic Analysis , 2020, J. Chem. Inf. Model..

[39]  Anthony P. F. Cook,et al.  Computer‐aided synthesis design: 40 years on , 2012 .

[40]  Noah P Tu,et al.  High-Throughput Reaction Screening with Nanomoles of Solid Reagents Coated on Glass Beads. , 2019, Angewandte Chemie.

[41]  Connor W. Coley,et al.  RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application , 2019, J. Chem. Inf. Model..

[42]  Richard J Ingham,et al.  Organic synthesis: march of the machines. , 2015, Angewandte Chemie.

[43]  F. Lombardo,et al.  Discovery of LSZ102, a Potent, Orally Bioavailable Selective Estrogen Receptor Degrader (SERD) for the Treatment of Estrogen Receptor Positive Breast Cancer. , 2018, Journal of medicinal chemistry.

[44]  Christopher J. Bartel,et al.  Machine learning for heterogeneous catalyst design and discovery , 2018 .

[45]  Alán Aspuru-Guzik,et al.  Phoenics: A Bayesian Optimizer for Chemistry , 2018, ACS central science.

[46]  J C Baber,et al.  Predicting synthetic accessibility: application in drug discovery and development. , 2004, Mini reviews in medicinal chemistry.

[47]  Bowen Liu,et al.  Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models , 2017, ACS central science.

[48]  William H. Green,et al.  Computer-Assisted Retrosynthesis Based on Molecular Similarity , 2017, ACS central science.

[49]  Peter Ertl,et al.  Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions , 2009, J. Cheminformatics.

[50]  C. Humblet,et al.  Idea2Data: Toward a New Paradigm for Drug Discovery. , 2019, ACS medicinal chemistry letters.

[51]  Ola Engkvist,et al.  Computational prediction of chemical reactions: current status and outlook. , 2018, Drug discovery today.

[52]  Derek T. Ahneman,et al.  Predicting reaction performance in C–N cross-coupling using machine learning , 2018, Science.

[53]  W. P. Walters,et al.  Virtual Chemical Libraries. , 2018, Journal of medicinal chemistry.

[54]  Leroy Cronin,et al.  How to explore chemical space using algorithms and automation , 2019, Nature Reviews Chemistry.

[55]  Regina Barzilay,et al.  Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network , 2017, NIPS.

[56]  Elizabeth Farrant,et al.  Rapid discovery of a novel series of Abl kinase inhibitors by application of an integrated microfluidic synthesis and screening platform. , 2013, Journal of medicinal chemistry.

[57]  Zheng Li,et al.  Toward artificial intelligence in catalysis , 2018, Nature Catalysis.

[58]  J. Reymond,et al.  Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc04944d , 2019, Chemical science.

[59]  William H. Green,et al.  Using Machine Learning To Predict Suitable Conditions for Organic Reactions , 2018, ACS central science.

[60]  Yang Liu,et al.  Route Designer: A Retrosynthetic Analysis Tool Utilizing Automated Retrosynthetic Rule Generation , 2009, J. Chem. Inf. Model..

[61]  Ola Engkvist,et al.  Neural Network Guided Tree-Search Policies for Synthesis Planning , 2019, ICANN.

[62]  Johann Gasteiger,et al.  Computer‐Assisted Planning of Organic Syntheses: The Second Generation of Programs , 1996 .

[63]  M. Parmentier,et al.  Selective Amidation of Unprotected Amino Alcohols Using Surfactant-in-Water Technology: A Highly Desirable Alternative to Reprotoxic Polar Aprotic Solvents , 2016 .

[64]  Connor W. Coley,et al.  A graph-convolutional neural network model for the prediction of chemical reactivity , 2018, Chemical science.

[65]  Ji-Bo Wang,et al.  A retrosynthetic analysis algorithm implementation , 2019, Journal of Cheminformatics.

[66]  Derek T. Ahneman,et al.  Deoxyfluorination with Sulfonyl Fluorides: Navigating Reaction Space with Machine Learning. , 2018, Journal of the American Chemical Society.

[67]  Lewis R. Vidler,et al.  Creating a Virtual Assistant for Medicinal Chemistry. , 2019, ACS medicinal chemistry letters.

[68]  G. Liang,et al.  Discovery of 3-(Indol-5-yl)-indazole Derivatives as Novel Myeloid Differentiation Protein 2/Toll-like Receptor 4 Antagonists for Treatment of Acute Lung Injury. , 2019, Journal of medicinal chemistry.

[69]  Leroy Cronin,et al.  Towards dial-a-molecule by integrating continuous flow, analytics and self-optimisation. , 2016, Chemical Society reviews.

[70]  Chi Heem Wong,et al.  Estimation of clinical trial success rates and related parameters , 2018, Biostatistics.

[71]  John S. Schreck,et al.  Learning Retrosynthetic Planning through Simulated Experience , 2019, ACS central science.

[72]  Alexander G. Godfrey,et al.  A remote-controlled adaptive medchem lab: an innovative approach to enable drug discovery in the 21st Century. , 2013, Drug discovery today.

[73]  Jun Li,et al.  Making Better Decisions During Synthetic Route Design: Leveraging Prediction to Achieve Greenness-by-Design , 2019, Reaction Chemistry & Engineering.

[74]  Kristian Birchall,et al.  Design, Synthesis, and Testing of Potent, Selective Hepsin Inhibitors via Application of an Automated Closed-Loop Optimization Platform. , 2018, Journal of medicinal chemistry.

[75]  D. Parry.  Closing the Loop: Developing an Integrated Design, Make, and Test Platform for Discovery. , 2019, ACS medicinal chemistry letters.

[76]  Igor V. Tetko,et al.  A Transformer Model for Retrosynthesis , 2019, ICANN.

[77]  Mike Preuss,et al.  Planning chemical syntheses with deep neural networks and symbolic AI , 2017, Nature.

[78]  Marwin H. S. Segler,et al.  Modelling Chemical Reasoning to Predict Reactions , 2016, Chemistry.

[79]  Regina Barzilay,et al.  Prediction of Organic Reaction Outcomes Using Machine Learning , 2017, ACS central science.

[80]  Alpha A Lee,et al.  Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space. , 2019, Chemical communications.

[81]  Dragos Horvath,et al.  Expert System for Predicting Reaction Conditions: The Michael Reaction Case , 2015, J. Chem. Inf. Model..

[82]  Philip Judson,et al.  Knowledge-based expert systems in chemistry : not counting on computers , 2009 .