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

Sridharan, Bhuvanesh; Goel, Manan

doi:10.1039/d1cc07035e

Cited by 23 publications

(23 citation statements)

References 131 publications

(166 reference statements)

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“…The task assigned to the “agent” is to choose an action given the current state. On the other hand, the role of the “environment” is to simulate the action which was chosen by the agent and return the reward for the action which was taken. , One such algorithm is Monte Carlo tree search (MCTS) (Figure ). MCTS performs one of the four following steps repeatedly:…”

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confidence: 99%

Deep Reinforcement Learning for Molecular Inverse Problem of Nuclear Magnetic Resonance Spectra to Molecular Structure

Sridharan

Mehta

Pathak

2022

J. Phys. Chem. Lett.

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Spectroscopy is the study of how matter interacts with electromagnetic radiation. The spectra of any molecule are highly information-rich, yet the inverse relation of spectra to the corresponding molecular structure is still an unsolved problem. Nuclear magnetic resonance (NMR) spectroscopy is one such critical technique in the scientists’ toolkit to characterize molecules. In this work, a novel machine learning framework is proposed that attempts to solve this inverse problem by navigating the chemical space to find the correct structure given an NMR spectra. The proposed framework uses a combination of online Monte Carlo tree search (MCTS) and a set of graph convolution networks to build a molecule iteratively. Our method can predict the structure of the molecule ∼80% of the time in its top 3 guesses for molecules with <10 heavy atoms. We believe that the proposed framework is a significant step in solving the inverse design problem of NMR spectra.

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confidence: 99%

Deep Reinforcement Learning for Molecular Inverse Problem of Nuclear Magnetic Resonance Spectra to Molecular Structure

Sridharan

Mehta

Pathak

2022

J. Phys. Chem. Lett.

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“…In computational design, forward design approaches involve predicting the properties of input structures after significant training on similar data . As an alternative, inverse design approaches yield novel structures based on input desired properties and characteristics . For sustainable material design, requirements should include optimization toward both the desired material properties (e.g., electrochemical potential, strength, and stiffness) and sustainability metrics (e.g., embodied carbon or embodied energy).…”

Section: Computational Methods For Materiomics and Sustainable Materi...mentioning

confidence: 99%

“…99 As an alternative, inverse design approaches yield novel structures based on input desired properties and characteristics. 100 For sustainable material design, requirements should include optimization toward both the desired material properties (e.g., electrochemical potential, strength, and stiffness) and sustainability metrics (e.g., embodied carbon or embodied energy). Machine learning tools that aid inverse design include genetic algorithms (GAs) and Bayesian optimization (BO).…”

Section: Tools For Inverse Design and Experimentalmentioning

confidence: 99%

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics

et al. 2023

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Engineered materials are ubiquitous throughout society and are critical to the development of modern technology, yet many current material systems are inexorably tied to widespread deterioration of ecological processes. Next-generation material systems can address goals of environmental sustainability by providing alternatives to fossil fuel-based materials and by reducing destructive extraction processes, energy costs, and accumulation of solid waste. However, development of sustainable materials faces several key challenges including investigation, processing, and architecting of new feedstocks that are often relatively mechanically weak, complex, and difficult to characterize or standardize. In this review paper, we outline a framework for examining sustainability in material systems and discuss how recent developments in modeling, machine learning, and other computational tools can aid the discovery of novel sustainable materials. We consider these through the lens of materiomics, an approach that considers material systems holistically by incorporating perspectives of all relevant scales, beginning with first-principles approaches and extending through the macroscale to consider sustainable material design from the bottom-up. We follow with an examination of how computational methods are currently applied to select examples of sustainable material development, with particular emphasis on bioinspired and biobased materials, and conclude with perspectives on opportunities and open challenges.

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“…[22][23][24] An interesting application of deep generative models is to be able to sample from marginal distributions of molecules based on given conditions. Normally termed as the inverse design of molecules, 25 the idea is to be able to generate molecules that have the same properties as specified at the time of sampling, that is, the generation of the molecule is conditioned on the specified property values. 23 This idea can be extended to learning distributions of molecules that are conditioned to bind well to a given target receptor.…”

Section: Generative Modelingmentioning

confidence: 99%

Efficient and enhanced sampling of drug‐like chemical space for virtual screening and molecular design using modern machine learning methods

Goel

Aggarwal

Sridharan

et al. 2022

WIREs Comput Mol Sci

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Drug design involves the process of identifying and designing novel molecules that have desirable properties and bind well to a given target receptor. Typically, such molecules are identified by screening large chemical libraries for desirable physicochemical properties and binding strength with the target protein. This traditional approach, however, has severe limitations as exhaustively screening every molecule in known chemical libraries is computationally infeasible. Furthermore, currently available molecular libraries are only a minuscule part of the entire set of possible drug-like molecular structures (drug-like chemical space). In this review, we discuss how the former limitation is addressed by modeling virtual screening as a search space problem and how these endeavors utilize machine learning to reduce the number of required computational experiments to identify top candidates. We follow that up by discussing generative methods that attempt to approximate the entire drug-like chemical space providing us a path to explore beyond the known drug-like chemical space. We place special emphasis on generative models that learn the marginal distributions conditioned on specific properties or receptor structures for efficient sampling of molecules. Through this review, we aim to highlight modern machine learning based methods that try to efficiently enhance our sampling capability beyond conventional screening methods which, in turn, would benefit drug design significantly. Therefore, we also encourage further methods of development that work on such important aspects of drug design.

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

Abstract: 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...

Cited by 23 publications

References 131 publications

Deep Reinforcement Learning for Molecular Inverse Problem of Nuclear Magnetic Resonance Spectra to Molecular Structure

Deep Reinforcement Learning for Molecular Inverse Problem of Nuclear Magnetic Resonance Spectra to Molecular Structure

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics

Efficient and enhanced sampling of drug‐like chemical space for virtual screening and molecular design using modern machine learning methods

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