Machine Learning for Pharmaceutical Discovery and Synthesis Consortium
is a collaboration between the pharmaceutical and biotechnology industries and the departments of Chemical Engineering, Chemistry, and Computer Science at the Massachusetts Institute of Technology. This collaboration will facilitate the design of useful software for the automation of small molecule discovery and synthesis.
The MIT Consortium, Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS), brings together computer scientists, chemical engineers, and chemists from MIT with scientists from member companies to create new data science and artificial intelligence algorithms along with tools to facilitate the discovery and synthesis of new therapeutics. MLPDS educates scientists and engineers to work effectively at the data science/chemistry interface and provides opportunities for member companies and MIT to collectively create, discuss, and evaluate new advances in data science for chemical and pharmaceutical discovery, development, and manufacturing.
Specific research topics within the consortium include synthesis planning; prediction of reaction outcomes, conditions, and impurities; prediction of molecular properties; molecular representation, generation, and optimization (de novo design); and extraction and organization of chemical information. The algorithms are developed and validated on public data and then transferred to member companies for application to proprietary data. All members share intellectual property and royalty free access to all developments. MIT endeavors to make tool development and transfer successful through one-on-one meetings and teleconferences with individual member companies, Microsoft Teams channels, GitLab software repositories, and consortium face-to-face meetings and teleconferences.