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Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction...

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Autores principales: Cova, Tânia F. G. G., Pais, Alberto A. C. C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6988795/
https://www.ncbi.nlm.nih.gov/pubmed/32039134
http://dx.doi.org/10.3389/fchem.2019.00809
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author Cova, Tânia F. G. G.
Pais, Alberto A. C. C.
author_facet Cova, Tânia F. G. G.
Pais, Alberto A. C. C.
author_sort Cova, Tânia F. G. G.
collection PubMed
description Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.
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spelling pubmed-69887952020-02-07 Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns Cova, Tânia F. G. G. Pais, Alberto A. C. C. Front Chem Chemistry Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly. Frontiers Media S.A. 2019-11-26 /pmc/articles/PMC6988795/ /pubmed/32039134 http://dx.doi.org/10.3389/fchem.2019.00809 Text en Copyright © 2019 Cova and Pais. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Chemistry
Cova, Tânia F. G. G.
Pais, Alberto A. C. C.
Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns
title Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns
title_full Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns
title_fullStr Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns
title_full_unstemmed Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns
title_short Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns
title_sort deep learning for deep chemistry: optimizing the prediction of chemical patterns
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6988795/
https://www.ncbi.nlm.nih.gov/pubmed/32039134
http://dx.doi.org/10.3389/fchem.2019.00809
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