Literature DB >> 31320117

Advancing Drug Discovery via Artificial Intelligence.

H C Stephen Chan1, Hanbin Shan2, Thamani Dahoun3, Horst Vogel3, Shuguang Yuan4.   

Abstract

Drug discovery and development are among the most important translational science activities that contribute to human health and wellbeing. However, the development of a new drug is a very complex, expensive, and long process which typically costs 2.6 billion USD and takes 12 years on average. How to decrease the costs and speed up new drug discovery has become a challenging and urgent question in industry. Artificial intelligence (AI) combined with new experimental technologies is expected to make the hunt for new pharmaceuticals quicker, cheaper, and more effective. We discuss here emerging applications of AI to improve the drug discovery process.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  QSAR; artificial intelligence; drug design; drug discovery; planning chemical syntheses; virtual screening

Mesh:

Substances:

Year:  2019        PMID: 31320117     DOI: 10.1016/j.tips.2019.06.004

Source DB:  PubMed          Journal:  Trends Pharmacol Sci        ISSN: 0165-6147            Impact factor:   14.819


  38 in total

1.  Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

Authors:  Kahini Wadhawan; Inkit Padhi; Sebastian Gehrmann; Payel Das; Tom Sercu; Flaviu Cipcigan; Vijil Chenthamarakshan; Hendrik Strobelt; Cicero Dos Santos; Pin-Yu Chen; Yi Yan Yang; Jeremy P K Tan; James Hedrick; Jason Crain; Aleksandra Mojsilovic
Journal:  Nat Biomed Eng       Date:  2021-03-11       Impact factor: 25.671

Review 2.  Advances and Challenges in Rational Drug Design for SLCs.

Authors:  Rachel-Ann A Garibsingh; Avner Schlessinger
Journal:  Trends Pharmacol Sci       Date:  2019-09-10       Impact factor: 14.819

3.  Artificial intelligence in clinical research of cancers.

Authors:  Dan Shao; Yinfei Dai; Nianfeng Li; Xuqing Cao; Wei Zhao; Li Cheng; Zhuqing Rong; Lan Huang; Yan Wang; Jing Zhao
Journal:  Brief Bioinform       Date:  2022-01-17       Impact factor: 11.622

Review 4.  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

Review 5.  Strategies for targeting the cardiac sarcomere: avenues for novel drug discovery.

Authors:  Joshua B Holmes; Chang Yoon Doh; Ranganath Mamidi; Jiayang Li; Julian E Stelzer
Journal:  Expert Opin Drug Discov       Date:  2020-02-18       Impact factor: 6.098

6.  AlloSigMA 2: paving the way to designing allosteric effectors and to exploring allosteric effects of mutations.

Authors:  Zhen Wah Tan; Enrico Guarnera; Wei-Ven Tee; Igor N Berezovsky
Journal:  Nucleic Acids Res       Date:  2020-07-02       Impact factor: 16.971

Review 7.  Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.

Authors:  Neetu Tripathi; Manoj Kumar Goshisht; Sanat Kumar Sahu; Charu Arora
Journal:  Mol Divers       Date:  2021-06-10       Impact factor: 2.943

8.  Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities.

Authors:  Nadia Terranova; Karthik Venkatakrishnan; Lisa J Benincosa
Journal:  AAPS J       Date:  2021-05-18       Impact factor: 4.009

Review 9.  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

10.  Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.

Authors:  Manish Kumar Tripathi; Abhigyan Nath; Tej P Singh; A S Ethayathulla; Punit Kaur
Journal:  Mol Divers       Date:  2021-06-23       Impact factor: 3.364

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.