Literature DB >> 29510217

Computational prediction of chemical reactions: current status and outlook.

Ola Engkvist1, Per-Ola Norrby2, Nidhal Selmi3, Yu-Hong Lam4, Zhengwei Peng4, Edward C Sherer4, Willi Amberg5, Thomas Erhard5, Lynette A Smyth5.   

Abstract

Over the past few decades, various computational methods have become increasingly important for discovering and developing novel drugs. Computational prediction of chemical reactions is a key part of an efficient drug discovery process. In this review, we discuss important parts of this field, with a focus on utilizing reaction data to build predictive models, the existing programs for synthesis prediction, and usage of quantum mechanics and molecular mechanics (QM/MM) to explore chemical reactions. We also outline potential future developments with an emphasis on pre-competitive collaboration opportunities.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2018        PMID: 29510217     DOI: 10.1016/j.drudis.2018.02.014

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  17 in total

Review 1.  Expanding the medicinal chemistry synthetic toolbox.

Authors:  Jonas Boström; Dean G Brown; Robert J Young; György M Keserü
Journal:  Nat Rev Drug Discov       Date:  2018-08-24       Impact factor: 84.694

2.  "Found in Translation": predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models.

Authors:  Philippe Schwaller; Théophile Gaudin; Dávid Lányi; Costas Bekas; Teodoro Laino
Journal:  Chem Sci       Date:  2018-06-22       Impact factor: 9.825

Review 3.  Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future.

Authors:  Andrew F Zahrt; Soumitra V Athavale; Scott E Denmark
Journal:  Chem Rev       Date:  2019-12-30       Impact factor: 60.622

Review 4.  Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Authors:  Rohan Gupta; Devesh Srivastava; Mehar Sahu; Swati Tiwari; Rashmi K Ambasta; Pravir Kumar
Journal:  Mol Divers       Date:  2021-04-12       Impact factor: 3.364

5.  Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction.

Authors:  Philippe Schwaller; Teodoro Laino; Théophile Gaudin; Peter Bolgar; Christopher A Hunter; Costas Bekas; Alpha A Lee
Journal:  ACS Cent Sci       Date:  2019-08-30       Impact factor: 14.553

Review 6.  Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research.

Authors:  Laurianne David; Josep Arús-Pous; Johan Karlsson; Ola Engkvist; Esben Jannik Bjerrum; Thierry Kogej; Jan M Kriegl; Bernd Beck; Hongming Chen
Journal:  Front Pharmacol       Date:  2019-11-05       Impact factor: 5.810

7.  Graph-based machine learning interprets and predicts diagnostic isomer-selective ion-molecule reactions in tandem mass spectrometry.

Authors:  Jonathan Fine; Judy Kuan-Yu Liu; Armen Beck; Kawthar Z Alzarieni; Xin Ma; Victoria M Boulos; Hilkka I Kenttämaa; Gaurav Chopra
Journal:  Chem Sci       Date:  2020-10-05       Impact factor: 9.825

8.  Models of necessity.

Authors:  Timothy Clark; Martin G Hicks
Journal:  Beilstein J Org Chem       Date:  2020-07-13       Impact factor: 2.883

Review 9.  Artificial Intelligence in Drug Design.

Authors:  Gerhard Hessler; Karl-Heinz Baringhaus
Journal:  Molecules       Date:  2018-10-02       Impact factor: 4.411

10.  Operando Modeling of Multicomponent Reactive Solutions in Homogeneous Catalysis: from Non-standard Free Energies to Reaction Network Control.

Authors:  Pavel O Kuliaev; Evgeny A Pidko
Journal:  ChemCatChem       Date:  2019-12-11       Impact factor: 5.686

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.