Modern machine learning for tackling inverse problems in chemistry: molecular design to realization

The discovery of new molecules and materials helps expand the horizons of novel and innovative real-life applications. In pursuit of finding molecules with desired properties, chemists have traditionally relied on experimentation and recently on combinatorial methods to generate new substances often...

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Bibliographic Details
Published in:Chemical communications (Cambridge, England) Vol. 58; no. 35; p. 5316
Main Authors: Sridharan, Bhuvanesh, Goel, Manan, Priyakumar, U Deva
Format: Journal Article
Language:English
Published: England 28.04.2022
Subjects:
Bayes Theorem
Deep Learning
Drug Design
Drug Discovery - methods
Machine Learning
ISSN:1364-548X, 1364-548X
Online Access:Get more information
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Summary:The discovery of new molecules and materials helps expand the horizons of novel and innovative real-life applications. In 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 are a variety of inverse problems in chemistry encompassing various subfields like drug discovery, retrosynthesis, structure identification, Recent developments in modern machine learning (ML) methods have shown great promise in tackling problems of this kind. This has helped in making major strides in all key phases of molecule discovery ranging from candidate generation to their synthesis with a 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 a purpose.
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ISSN:1364-548X
1364-548X
DOI:10.1039/d1cc07035e