Modern Machine Learning for Tackling Inverse Problems in Chemistry: Molecular Design to Realization

Chemical Communications Journal

The discovery of new molecules and materials helps expand the horizons of novel and innovative real-life applications. In the pursuit of finding molecules with desired properties, chemists have traditionally relied on experimentation and recently on combinatorial methods to generate new substances often complimented by computational methods. The sheer size of the chemical space makes it infeasible to search through all possible molecules exhaustively. This calls for fast and efficient methods to navigate the chemical space to find substances with desired properties. This class of problems is referred to as inverse design problems. There is a variety of inverse problems in chemistry encompassing various subfields like drug discovery, retrosynthesis, structure identification etc. Recent developments in modern machine learning (ML) methods have shown great promise in being able to tackle problems of this kind. This has helped in making major strides in all key phases of molecule discovery ranging from in silico candidate generation to their synthesis with focus on small organic molecules. Optimization techniques like Bayesian optimization, reinforcement learning, attention-based transformers, deep generative models like variational autoencoders and generative adversarial networks form a robust arsenal of methods. This highlight summarizes the development of deep learning to tackle a wide variety of inverse design problems in chemistry towards the quest for synthesizing small organic compounds with purpose.

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Modern Machine Learning for Tackling Inverse Problems in Chemistry: Molecular Design to Realization

Bhuvanesh Sridharan, Manan Goel and U. Deva Priyakumar

Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India

Chemical Communications Journal
https://doi.org/10.1039/D1CC07035E

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