Literature DB >> 30429945

Transforming Computational Drug Discovery with Machine Learning and AI.

Justin S Smith1,2,3, Adrian E Roitberg1, Olexandr Isayev4.   

Abstract

In this Viewpoint, we discuss the current progress in applications of machine learning (ML) and artificial intelligence (AI) to meet the challenges of computational drug discovery. We identify several areas where existing methods have the potential to accelerate pharmaceutical research and disrupt more traditional approaches.

Entities:  

Year:  2018        PMID： 30429945      PMCID： PMC6231187          DOI： 10.1021/acsmedchemlett.8b00437

Source DB:  PubMed          Journal:  ACS Med Chem Lett        ISSN： 1948-5875            Impact factor:   4.345


  10 in total

1.  A Comparison of Quantum and Molecular Mechanical Methods to Estimate Strain Energy in Druglike Fragments.

Authors:  Benjamin D Sellers; Natalie C James; Alberto Gobbi
Journal:  J Chem Inf Model       Date:  2017-05-17       Impact factor: 4.956

2.  Less is more: Sampling chemical space with active learning.

Authors:  Justin S Smith; Ben Nebgen; Nicholas Lubbers; Olexandr Isayev; Adrian E Roitberg
Journal:  J Chem Phys       Date:  2018-06-28       Impact factor: 3.488

Review 3.  Inverse molecular design using machine learning: Generative models for matter engineering.

Authors:  Benjamin Sanchez-Lengeling; Alán Aspuru-Guzik
Journal:  Science       Date:  2018-07-26       Impact factor: 47.728

4.  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

5.  ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules.

Authors:  Justin S Smith; Olexandr Isayev; Adrian E Roitberg
Journal:  Sci Data       Date:  2017-12-19       Impact factor: 6.444

6.  Machine learning molecular dynamics for the simulation of infrared spectra.

Authors:  Michael Gastegger; Jörg Behler; Philipp Marquetand
Journal:  Chem Sci       Date:  2017-08-10       Impact factor: 9.825

7.  ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost.

Authors:  J S Smith; O Isayev; A E Roitberg
Journal:  Chem Sci       Date:  2017-02-08       Impact factor: 9.825

8.  Deep reinforcement learning for de novo drug design.

Authors:  Mariya Popova; Olexandr Isayev; Alexander Tropsha
Journal:  Sci Adv       Date:  2018-07-25       Impact factor: 14.136

9.  Controlling an organic synthesis robot with machine learning to search for new reactivity.

Authors:  Jarosław M Granda; Liva Donina; Vincenza Dragone; De-Liang Long; Leroy Cronin
Journal:  Nature       Date:  2018-07-18       Impact factor: 49.962

Review 10.  Machine learning for molecular and materials science.

Authors:  Keith T Butler; Daniel W Davies; Hugh Cartwright; Olexandr Isayev; Aron Walsh
Journal:  Nature       Date:  2018-07-25       Impact factor: 49.962
  10 in total
  11 in total

Review 1.  Applications of machine learning in drug discovery and development.

Authors:  Jessica Vamathevan; Dominic Clark; Paul Czodrowski; Ian Dunham; Edgardo Ferran; George Lee; Bin Li; Anant Madabhushi; Parantu Shah; Michaela Spitzer; Shanrong Zhao
Journal:  Nat Rev Drug Discov       Date:  2019-06       Impact factor: 84.694

Review 2.  Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

Authors:  Vertika Gautam; Anand Gaurav; Neeraj Masand; Vannajan Sanghiran Lee; Vaishali M Patil
Journal:  Mol Divers       Date:  2022-07-11       Impact factor: 3.364

3.  Memory augmented recurrent neural networks for de-novo drug design.

Authors:  Naveen Suresh; Neelesh Chinnakonda Ashok Kumar; Srikumar Subramanian; Gowri Srinivasa
Journal:  PLoS One       Date:  2022-06-23       Impact factor: 3.752

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

Review 5.  Contribution of Organofluorine Compounds to Pharmaceuticals.

Authors:  Munenori Inoue; Yuji Sumii; Norio Shibata
Journal:  ACS Omega       Date:  2020-04-22

Review 6.  Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns.

Authors:  Tânia F G G Cova; Alberto A C C Pais
Journal:  Front Chem       Date:  2019-11-26       Impact factor: 5.221

Review 7.  Deep learning tools for advancing drug discovery and development.

Authors:  Sagorika Nag; Anurag T K Baidya; Abhimanyu Mandal; Alen T Mathew; Bhanuranjan Das; Bharti Devi; Rajnish Kumar
Journal:  3 Biotech       Date:  2022-04-09       Impact factor: 2.893

8.  Dataset Construction to Explore Chemical Space with 3D Geometry and Deep Learning.

Authors:  Jianing Lu; Song Xia; Jieyu Lu; Yingkai Zhang
Journal:  J Chem Inf Model       Date:  2021-03-08       Impact factor: 4.956

9.  Mycobacterium tuberculosis Cell Wall Permeability Model Generation Using Chemoinformatics and Machine Learning Approaches.

Authors:  Selvaraman Nagamani; G Narahari Sastry
Journal:  ACS Omega       Date:  2021-06-25

Review 10.  Alkaloids in Contemporary Drug Discovery to Meet Global Disease Needs.

Authors:  Sharna-Kay Daley; Geoffrey A Cordell
Journal:  Molecules       Date:  2021-06-22       Impact factor: 4.411
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