Literature DB >> 31377227

Deep learning in drug discovery: opportunities, challenges and future prospects.

Antonio Lavecchia1.   

Abstract

Artificial Intelligence (AI) is an area of computer science that simulates the structures and operating principles of the human brain. Machine learning (ML) belongs to the area of AI and endeavors to develop models from exposure to training data. Deep Learning (DL) is another subset of AI, where models represent geometric transformations over many different layers. This technology has shown tremendous potential in areas such as computer vision, speech recognition and natural language processing. More recently, DL has also been successfully applied in drug discovery. Here, I analyze several relevant DL applications and case studies, providing a detailed view of the current state-of-the-art in drug discovery and highlighting not only the problematic issues, but also the successes and opportunities for further advances.
Copyright © 2019 Elsevier Ltd. All rights reserved.

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Year:  2019        PMID: 31377227     DOI: 10.1016/j.drudis.2019.07.006

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


  22 in total

1.  Prediction of Drug Clearance from Enzyme and Transporter Kinetics.

Authors:  Priyanka R Kulkarni; Amir S Youssef; Aneesh A Argikar
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Review 2.  An introduction to machine learning and analysis of its use in rheumatic diseases.

Authors:  Kathryn M Kingsmore; Christopher E Puglisi; Amrie C Grammer; Peter E Lipsky
Journal:  Nat Rev Rheumatol       Date:  2021-11-02       Impact factor: 20.543

3.  Cell morphology-based machine learning models for human cell state classification.

Authors:  Yi Li; Chance M Nowak; Uyen Pham; Khai Nguyen; Leonidas Bleris
Journal:  NPJ Syst Biol Appl       Date:  2021-05-26

Review 4.  Representation of molecules for drug response prediction.

Authors:  Xin An; Xi Chen; Daiyao Yi; Hongyang Li; Yuanfang Guan
Journal:  Brief Bioinform       Date:  2022-01-17       Impact factor: 13.994

5.  Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors.

Authors:  Xuanyi Li; Yinqiu Xu; Hequan Yao; Kejiang Lin
Journal:  J Cheminform       Date:  2020-06-08       Impact factor: 5.514

6.  Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery.

Authors:  Lun K Tsou; Shiu-Hwa Yeh; Shau-Hua Ueng; Chun-Ping Chang; Jen-Shin Song; Mine-Hsine Wu; Hsiao-Fu Chang; Sheng-Ren Chen; Chuan Shih; Chiung-Tong Chen; Yi-Yu Ke
Journal:  Sci Rep       Date:  2020-10-08       Impact factor: 4.379

Review 7.  Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development.

Authors:  Arash Keshavarzi Arshadi; Julia Webb; Milad Salem; Emmanuel Cruz; Stacie Calad-Thomson; Niloofar Ghadirian; Jennifer Collins; Elena Diez-Cecilia; Brendan Kelly; Hani Goodarzi; Jiann Shiun Yuan
Journal:  Front Artif Intell       Date:  2020-08-18

Review 8.  Advances in de Novo Drug Design: From Conventional to Machine Learning Methods.

Authors:  Varnavas D Mouchlis; Antreas Afantitis; Angela Serra; Michele Fratello; Anastasios G Papadiamantis; Vassilis Aidinis; Iseult Lynch; Dario Greco; Georgia Melagraki
Journal:  Int J Mol Sci       Date:  2021-02-07       Impact factor: 5.923

9.  Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions.

Authors:  Raquel Rodríguez-Pérez; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2021-02-17       Impact factor: 3.686

Review 10.  How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs.

Authors:  Mariangela Garofalo; Giovanni Grazioso; Andrea Cavalli; Jacopo Sgrignani
Journal:  Molecules       Date:  2020-04-10       Impact factor: 4.411

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