Literature DB >> 35199553

The potential applications of artificial intelligence in drug discovery and development.

H Farghali1, N Kutinová Canová, M Arora.   

Abstract

Development of a new dug is a very lengthy and highly expensive process since only preclinical, pharmacokinetic, pharmacodynamic and toxicological studies include a multiple of in silico, in vitro, in vivo experimentations that traditionally last several years. In the present review, we briefly report some examples that demonstrate the power of the computer-assisted drug discovery process with some examples that are published and revealing the successful applications of artificial intelligence (AI) technology on this vivid area. Besides, we address the situation of drug repositioning (repurposing) in clinical applications. Yet few success stories in this regard that provide us with a clear evidence that AI will reveal its great potential in accelerating effective new drug finding. AI accelerates drug repurposing and AI approaches are altogether necessary and inevitable tools in new medicine development. In spite of the fact that AI in drug development is still in its infancy, the advancements in AI and machine-learning (ML) algorithms have an unprecedented potential. The AI/ML solutions driven by pharmaceutical scientists, computer scientists, statisticians, physicians and others are increasingly working together in the processes of drug development and are adopting AI-based technologies for the rapid discovery of medicines. AI approaches, coupled with big data, are expected to substantially improve the effectiveness of drug repurposing and finding new drugs for various complex human diseases.

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Year:  2021        PMID: 35199553      PMCID: PMC9054182          DOI: 10.33549/physiolres.934765

Source DB:  PubMed          Journal:  Physiol Res        ISSN: 0862-8408            Impact factor:   2.139


  18 in total

1.  A Study on the Application and Use of Artificial Intelligence to Support Drug Development.

Authors:  Mary Jo Lamberti; Michael Wilkinson; Bruce A Donzanti; G Erich Wohlhieter; Sudip Parikh; Robert G Wilkins; Ken Getz
Journal:  Clin Ther       Date:  2019-06-24       Impact factor: 3.393

2.  A Deep Learning Approach to Antibiotic Discovery.

Authors:  Jonathan M Stokes; Kevin Yang; Kyle Swanson; Wengong Jin; Andres Cubillos-Ruiz; Nina M Donghia; Craig R MacNair; Shawn French; Lindsey A Carfrae; Zohar Bloom-Ackermann; Victoria M Tran; Anush Chiappino-Pepe; Ahmed H Badran; Ian W Andrews; Emma J Chory; George M Church; Eric D Brown; Tommi S Jaakkola; Regina Barzilay; James J Collins
Journal:  Cell       Date:  2020-04-16       Impact factor: 41.582

3.  Modeling the Bioactivation and Subsequent Reactivity of Drugs.

Authors:  Tyler B Hughes; Noah Flynn; Na Le Dang; S Joshua Swamidass
Journal:  Chem Res Toxicol       Date:  2021-01-26       Impact factor: 3.739

Review 4.  Discovering Anti-Cancer Drugs via Computational Methods.

Authors:  Wenqiang Cui; Adnane Aouidate; Shouguo Wang; Qiuliyang Yu; Yanhua Li; Shuguang Yuan
Journal:  Front Pharmacol       Date:  2020-05-20       Impact factor: 5.810

5.  DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials.

Authors:  Arash Keshavarzi Arshadi; Milad Salem; Jennifer Collins; Jiann Shiun Yuan; Debopam Chakrabarti
Journal:  Front Pharmacol       Date:  2020-01-15       Impact factor: 5.810

6.  Accurate prediction of protein structures and interactions using a three-track neural network.

Authors:  Minkyung Baek; Frank DiMaio; Ivan Anishchenko; Justas Dauparas; Sergey Ovchinnikov; Gyu Rie Lee; Jue Wang; Qian Cong; Lisa N Kinch; R Dustin Schaeffer; Claudia Millán; Hahnbeom Park; Carson Adams; Caleb R Glassman; Andy DeGiovanni; Jose H Pereira; Andria V Rodrigues; Alberdina A van Dijk; Ana C Ebrecht; Diederik J Opperman; Theo Sagmeister; Christoph Buhlheller; Tea Pavkov-Keller; Manoj K Rathinaswamy; Udit Dalwadi; Calvin K Yip; John E Burke; K Christopher Garcia; Nick V Grishin; Paul D Adams; Randy J Read; David Baker
Journal:  Science       Date:  2021-07-15       Impact factor: 47.728

Review 7.  Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.

Authors:  Sezen Vatansever; Avner Schlessinger; Daniel Wacker; H Ümit Kaniskan; Jian Jin; Ming-Ming Zhou; Bin Zhang
Journal:  Med Res Rev       Date:  2020-12-09       Impact factor: 12.944

Review 8.  Artificial intelligence in COVID-19 drug repurposing.

Authors:  Yadi Zhou; Fei Wang; Jian Tang; Ruth Nussinov; Feixiong Cheng
Journal:  Lancet Digit Health       Date:  2020-09-18

9.  A Deep Learning Approach to Antibiotic Discovery.

Authors:  Jonathan M Stokes; Kevin Yang; Kyle Swanson; Wengong Jin; Andres Cubillos-Ruiz; Nina M Donghia; Craig R MacNair; Shawn French; Lindsey A Carfrae; Zohar Bloom-Ackermann; Victoria M Tran; Anush Chiappino-Pepe; Ahmed H Badran; Ian W Andrews; Emma J Chory; George M Church; Eric D Brown; Tommi S Jaakkola; Regina Barzilay; James J Collins
Journal:  Cell       Date:  2020-02-20       Impact factor: 41.582

10.  Highly accurate protein structure prediction with AlphaFold.

Authors:  John Jumper; Richard Evans; Alexander Pritzel; Tim Green; Michael Figurnov; Olaf Ronneberger; Kathryn Tunyasuvunakool; Russ Bates; Augustin Žídek; Anna Potapenko; Alex Bridgland; Clemens Meyer; Simon A A Kohl; Andrew J Ballard; Andrew Cowie; Bernardino Romera-Paredes; Stanislav Nikolov; Rishub Jain; Demis Hassabis; Jonas Adler; Trevor Back; Stig Petersen; David Reiman; Ellen Clancy; Michal Zielinski; Martin Steinegger; Michalina Pacholska; Tamas Berghammer; Sebastian Bodenstein; David Silver; Oriol Vinyals; Andrew W Senior; Koray Kavukcuoglu; Pushmeet Kohli
Journal:  Nature       Date:  2021-07-15       Impact factor: 49.962

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  1 in total

1.  A New Approach to Drug Repurposing with Two-Stage Prediction, Machine Learning, and Unsupervised Clustering of Gene Expression.

Authors:  Yi Cong; Misaki Shintani; Fuga Imanari; Naoki Osada; Toshinori Endo
Journal:  OMICS       Date:  2022-06-03
  1 in total

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