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Deep Reinforcement Learning for Molecular Inverse Problem of Nuclear Magnetic Resonance Spectra to Molecular Structure.
Sridharan, Bhuvanesh; Mehta, Sarvesh; Pathak, Yashaswi; Priyakumar, U Deva.
Afiliação
  • Sridharan B; Centre for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India.
  • Mehta S; Centre for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India.
  • Pathak Y; Centre for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India.
  • Priyakumar UD; Centre for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India.
J Phys Chem Lett ; 13(22): 4924-4933, 2022 Jun 09.
Article em En | MEDLINE | ID: mdl-35635003
ABSTRACT
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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Phys Chem Lett Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Índia

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Phys Chem Lett Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Índia
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