Literature DB >> 30472429

Artificial intelligence in drug development: present status and future prospects.

Kit-Kay Mak1, Mallikarjuna Rao Pichika2.   

Abstract

Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. Remarkable improvements in computational power coupled with advancements in AI technology could be utilised to revolutionise the drug development process. At present, the pharmaceutical industry is facing challenges in sustaining their drug development programmes because of increased R&D costs and reduced efficiency. In this review, we discuss the major causes of attrition rates in new drug approvals, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms.
Copyright © 2018 Elsevier Ltd. All rights reserved.

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Year:  2018        PMID: 30472429     DOI: 10.1016/j.drudis.2018.11.014

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


  48 in total

Review 1.  Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.

Authors:  Linlin Zhao; Heather L Ciallella; Lauren M Aleksunes; Hao Zhu
Journal:  Drug Discov Today       Date:  2020-07-11       Impact factor: 7.851

2.  DeepScreening: a deep learning-based screening web server for accelerating drug discovery.

Authors:  Zhihong Liu; Jiewen Du; Jiansong Fang; Yulong Yin; Guohuan Xu; Liwei Xie
Journal:  Database (Oxford)       Date:  2019-01-01       Impact factor: 3.451

3.  Insight into potent TLR2 inhibitors for the treatment of disease caused by Mycoplasma pneumoniae based on machine learning approaches.

Authors:  Muhammad Ishfaq; Ziaur Rahman; Muhammad Aamir; Ihsan Ali; Yurong Guan; Zhihua Hu
Journal:  Mol Divers       Date:  2022-04-29       Impact factor: 2.943

4.  Identification of potential matrix metalloproteinase-2 inhibitors from natural products through advanced machine learning-based cheminformatics approaches.

Authors:  Ruoqi Yang; Guiping Zhao; Bin Cheng; Bin Yan
Journal:  Mol Divers       Date:  2022-06-30       Impact factor: 2.943

5.  Using digital technologies in clinical trials: Current and future applications.

Authors:  Carmen Rosa; Lisa A Marsch; Erin L Winstanley; Meg Brunner; Aimee N C Campbell
Journal:  Contemp Clin Trials       Date:  2020-11-17       Impact factor: 2.226

Review 6.  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

Review 7.  Artificial intelligence in dermatology and healthcare: An overview.

Authors:  Varadraj Vasant Pai; Rohini Bhat Pai
Journal:  Indian J Dermatol Venereol Leprol       Date:  2021 [SEASON]       Impact factor: 2.545

Review 8.  In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.

Authors:  Lauro Ribeiro de Souza Neto; José Teófilo Moreira-Filho; Bruno Junior Neves; Rocío Lucía Beatriz Riveros Maidana; Ana Carolina Ramos Guimarães; Nicholas Furnham; Carolina Horta Andrade; Floriano Paes Silva
Journal:  Front Chem       Date:  2020-02-18       Impact factor: 5.221

Review 9.  Prediction of antischistosomal small molecules using machine learning in the era of big data.

Authors:  Samuel K Kwofie; Kwasi Agyenkwa-Mawuli; Emmanuel Broni; Whelton A Miller Iii; Michael D Wilson
Journal:  Mol Divers       Date:  2021-08-05       Impact factor: 2.943

Review 10.  Translational precision medicine: an industry perspective.

Authors:  Dominik Hartl; Valeria de Luca; Anna Kostikova; Jason Laramie; Scott Kennedy; Enrico Ferrero; Richard Siegel; Martin Fink; Sohail Ahmed; John Millholland; Alexander Schuhmacher; Markus Hinder; Luca Piali; Adrian Roth
Journal:  J Transl Med       Date:  2021-06-05       Impact factor: 5.531

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