Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis
Thomas J. Struble, Juan C. Alvarez, Scott P. Brown, Milan Chytil, Justin S. Cisar, Renée L. DesJarlais, Ola Engkvist, Scott A. Frank, Daniel R. Greve, Daniel J. Griffin, Xinjun Hou, Jeffrey W. Johannes, Constantine Kreatsoulas, Brian R. Lahue, Miriam Mathea, Georg Mogk, Christos A. Nicolaou, Andrew Palmer, Daniel J. Price, Richard I. Robinson, Sebastian Salentin, Xing Li, Tommi Jaakkola, William H. Green, Regina Barzilay, Connor W. Coley, Klavs F. Jensen
Abstract
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.