Literature DB >> 26881908

De Novo Design at the Edge of Chaos.

Petra Schneider1,2, Gisbert Schneider1.   

Abstract

Computational medicinal chemistry offers viable strategies for finding, characterizing, and optimizing innovative pharmacologically active compounds. Technological advances in both computer hardware and software as well as biological chemistry have enabled a renaissance of computer-assisted "de novo" design of molecules with desired pharmacological properties. Here, we present our current perspective on the concept of automated molecule generation by highlighting chemocentric methods that may capture druglike chemical space, consider ligand promiscuity for hit and lead finding, and provide fresh ideas for the rational design of customized screening of compound libraries.

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Year:  2016        PMID: 26881908     DOI: 10.1021/acs.jmedchem.5b01849

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  29 in total

1.  Molecular Scaffold Hopping via Holistic Molecular Representation.

Authors:  Francesca Grisoni; Gisbert Schneider
Journal:  Methods Mol Biol       Date:  2021

Review 2.  Automating drug discovery.

Authors:  Gisbert Schneider
Journal:  Nat Rev Drug Discov       Date:  2017-12-15       Impact factor: 84.694

3.  Planning chemical syntheses with deep neural networks and symbolic AI.

Authors:  Marwin H S Segler; Mike Preuss; Mark P Waller
Journal:  Nature       Date:  2018-03-28       Impact factor: 49.962

4.  Optimization of Pyrazoles as Phenol Surrogates to Yield Potent Inhibitors of Macrophage Migration Inhibitory Factor.

Authors:  Vinay Trivedi-Parmar; Michael J Robertson; José A Cisneros; Stefan G Krimmer; William L Jorgensen
Journal:  ChemMedChem       Date:  2018-04-23       Impact factor: 3.466

5.  A virtual drug-screening approach to conquer huge chemical libraries.

Authors:  Charlotte Deane; Maranga Mokaya
Journal:  Nature       Date:  2022-01       Impact factor: 49.962

6.  Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.

Authors:  Rocco Meli; Garrett M Morris; Philip C Biggin
Journal:  Front Bioinform       Date:  2022-06-17

7.  Discovery of SARS-CoV-2 main protease inhibitors using a synthesis-directed de novo design model.

Authors:  Aaron Morris; William McCorkindale; The Covid Moonshot Consortium; Nir Drayman; John D Chodera; Savaş Tay; Nir London; Alpha A Lee
Journal:  Chem Commun (Camb)       Date:  2021-06-15       Impact factor: 6.222

Review 8.  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

Review 9.  Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence.

Authors:  José T Moreira-Filho; Arthur C Silva; Rafael F Dantas; Barbara F Gomes; Lauro R Souza Neto; Jose Brandao-Neto; Raymond J Owens; Nicholas Furnham; Bruno J Neves; Floriano P Silva-Junior; Carolina H Andrade
Journal:  Front Immunol       Date:  2021-05-31       Impact factor: 7.561

Review 10.  Opportunities and obstacles for deep learning in biology and medicine.

Authors:  Travers Ching; Daniel S Himmelstein; Brett K Beaulieu-Jones; Alexandr A Kalinin; Brian T Do; Gregory P Way; Enrico Ferrero; Paul-Michael Agapow; Michael Zietz; Michael M Hoffman; Wei Xie; Gail L Rosen; Benjamin J Lengerich; Johnny Israeli; Jack Lanchantin; Stephen Woloszynek; Anne E Carpenter; Avanti Shrikumar; Jinbo Xu; Evan M Cofer; Christopher A Lavender; Srinivas C Turaga; Amr M Alexandari; Zhiyong Lu; David J Harris; Dave DeCaprio; Yanjun Qi; Anshul Kundaje; Yifan Peng; Laura K Wiley; Marwin H S Segler; Simina M Boca; S Joshua Swamidass; Austin Huang; Anthony Gitter; Casey S Greene
Journal:  J R Soc Interface       Date:  2018-04       Impact factor: 4.293

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