Literature DB >> 31518513

Big Data and Artificial Intelligence Modeling for Drug Discovery.

Hao Zhu1.   

Abstract

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.

Entities:  

Keywords:  artificial intelligence; big data; computer-aided drug discovery; deep learning; machine learning; rational drug design

Mesh:

Year:  2019        PMID: 31518513      PMCID: PMC7010403          DOI: 10.1146/annurev-pharmtox-010919-023324

Source DB:  PubMed          Journal:  Annu Rev Pharmacol Toxicol        ISSN: 0362-1642            Impact factor:   13.820


  134 in total

1.  A widely applicable set of descriptors.

Authors:  P Labute
Journal:  J Mol Graph Model       Date:  2000 Aug-Oct       Impact factor: 2.518

2.  On outliers and activity cliffs--why QSAR often disappoints.

Authors:  Gerald M Maggiora
Journal:  J Chem Inf Model       Date:  2006 Jul-Aug       Impact factor: 4.956

3.  Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis.

Authors:  Hao Zhu; Alexander Tropsha; Denis Fourches; Alexandre Varnek; Ester Papa; Paola Gramatica; Tomas Oberg; Phuong Dao; Artem Cherkasov; Igor V Tetko
Journal:  J Chem Inf Model       Date:  2008-03-01       Impact factor: 4.956

Review 4.  Toward the development of "nano-QSARs": advances and challenges.

Authors:  Tomasz Puzyn; Danuta Leszczynska; Jerzy Leszczynski
Journal:  Small       Date:  2009-11       Impact factor: 13.281

5.  Accurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional Networks.

Authors:  Markus Hofmarcher; Elisabeth Rumetshofer; Djork-Arné Clevert; Sepp Hochreiter; Günter Klambauer
Journal:  J Chem Inf Model       Date:  2019-03-06       Impact factor: 4.956

Review 6.  Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity.

Authors:  Heather L Ciallella; Hao Zhu
Journal:  Chem Res Toxicol       Date:  2019-03-25       Impact factor: 3.739

7.  GCAC: galaxy workflow system for predictive model building for virtual screening.

Authors:  Deepak R Bharti; Anmol J Hemrom; Andrew M Lynn
Journal:  BMC Bioinformatics       Date:  2019-02-04       Impact factor: 3.169

8.  Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big Data.

Authors:  Marlene Thai Kim; Ruili Huang; Alexander Sedykh; Wenyi Wang; Menghang Xia; Hao Zhu
Journal:  Environ Health Perspect       Date:  2015-09-18       Impact factor: 9.031

9.  Development and evaluation of a deep learning model for protein-ligand binding affinity prediction.

Authors:  Marta M Stepniewska-Dziubinska; Piotr Zielenkiewicz; Pawel Siedlecki
Journal:  Bioinformatics       Date:  2018-11-01       Impact factor: 6.937

10.  Deep learning-based transcriptome data classification for drug-target interaction prediction.

Authors:  Lingwei Xie; Song He; Xinyu Song; Xiaochen Bo; Zhongnan Zhang
Journal:  BMC Genomics       Date:  2018-09-24       Impact factor: 3.969

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  28 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.  Machine Learning Models for Predicting Liver Toxicity.

Authors:  Jie Liu; Wenjing Guo; Sugunadevi Sakkiah; Zuowei Ji; Gokhan Yavas; Wen Zou; Minjun Chen; Weida Tong; Tucker A Patterson; Huixiao Hong
Journal:  Methods Mol Biol       Date:  2022

Review 3.  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

4.  Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay.

Authors:  Xuelian Jia; Xia Wen; Daniel P Russo; Lauren M Aleksunes; Hao Zhu
Journal:  J Hazard Mater       Date:  2022-05-20       Impact factor: 14.224

Review 5.  Review on the COVID-19 pandemic prevention and control system based on AI.

Authors:  Junfei Yi; Hui Zhang; Jianxu Mao; Yurong Chen; Hang Zhong; Yaonan Wang
Journal:  Eng Appl Artif Intell       Date:  2022-07-11       Impact factor: 7.802

Review 6.  Merging data curation and machine learning to improve nanomedicines.

Authors:  Chen Chen; Zvi Yaari; Elana Apfelbaum; Piotr Grodzinski; Yosi Shamay; Daniel A Heller
Journal:  Adv Drug Deliv Rev       Date:  2022-02-18       Impact factor: 17.873

Review 7.  New perspectives in cancer drug development: computational advances with an eye to design.

Authors:  Matteo Castelli; Stefano A Serapian; Filippo Marchetti; Alice Triveri; Valentina Pirota; Luca Torielli; Simona Collina; Filippo Doria; Mauro Freccero; Giorgio Colombo
Journal:  RSC Med Chem       Date:  2021-07-07

8.  Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids.

Authors:  Xuelian Jia; Heather L Ciallella; Daniel P Russo; Linlin Zhao; Morgan H James; Hao Zhu
Journal:  ACS Sustain Chem Eng       Date:  2021-03-04       Impact factor: 8.198

Review 9.  Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence.

Authors:  José T Moreira-Filho; Arthur C Silva; Rafael F Dantas; Barbara F Gomes; Lauro R Souza Neto; Jose Brandao-Neto; Raymond J Owens; Nicholas Furnham; Bruno J Neves; Floriano P Silva-Junior; Carolina H Andrade
Journal:  Front Immunol       Date:  2021-05-31       Impact factor: 7.561

10.  Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach.

Authors:  Heather L Ciallella; Daniel P Russo; Lauren M Aleksunes; Fabian A Grimm; Hao Zhu
Journal:  Environ Sci Technol       Date:  2021-07-25       Impact factor: 11.357

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