Molecular Machine Learning for Chemical Catalysis by Dr. Sukriti Singh

Venue: SIT001

Abstract: The development of new reaction methodology could become a tedious task demanding both time and resources. The application of machine learning (ML) approaches for reaction optimization and prediction can make a significant impact on efficient exploration of the high-dimensional chemical space. But the direct adaptation of ML as used in well-developed domains, is unlikely to succeed in reaction discovery. Some of the challenges stem from ineffective featurization of the molecular space, unavailability of quality data and its distribution. Given these backgrounds, rendering ML tools conducive for reactions is an exciting as well as challenging endeavor at the same time.

In this talk, I will present molecular machine learning strategies specifically designed for small-data reaction discovery, typically involving only hundreds to a few thousand data points. I will first discuss feature engineering approaches based on quantum-chemically derived physical organic descriptors, illustrated through catalytic asymmetric hydrogenation of imines and alkenes for predicting enantioselectivity. I will then introduce feature learning methods that learn molecular representations directly from data and demonstrate their effectiveness in predicting reaction yield and enantioselectivity across diverse catalytic transformations.

To address data scarcity, I will describe a transfer learning framework in which a chemical language model is trained on large number of molecules and fine-tuned on a focused library of desired reactions. Finally, I will briefly introduce a meta-learning workflow that leverages literature-derived reaction data to identify shared reaction features, enabling accurate outcome prediction with only a few experimental examples. Overall, this talk will highlight how technically sound deployment of molecular machine learning tools can guide reaction development and help us get closer to sustainable practices by reducing the number of heuristic and empirical steps.

Bio: Sukriti Singh received her M.Sc. and Ph.D. degrees from the Department of Chemistry, IIT Bombay in 2022 under the supervision of Prof. Raghavan B. Sunoj. She received the Naik and Rastogi Award for Excellence in Ph.D. Research. She was a postdoctoral research associate with Prof. J. M. Hernandez-Lobato at the Department of Engineering, University of Cambridge. Her research interests involve density functional theory studies of catalytic reactions and developing machine learning methods to tackle low-data situations aimed at accelerating the exploration of chemical reaction space of high contemporary interest.