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Literature DB >> 30269508

Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry.

Alex Zhavoronkov¹.

Abstract

Mesh：

Substances：
Biomarkers

Year: 2018 PMID： 30269508 DOI： 10.1021/acs.molpharmaceut.8b00930

Source DB: PubMed Journal: Mol Pharm ISSN： 1543-8384 Impact factor: 4.939

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Cited

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19 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. Exploiting machine learning for end-to-end drug discovery and development.

Authors: Sean Ekins; Ana C Puhl; Kimberley M Zorn; Thomas R Lane; Daniel P Russo; Jennifer J Klein; Anthony J Hickey; Alex M Clark
Journal: Nat Mater Date: 2019-04-18 Impact factor: 43.841

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. Harnessing the power of microscopy images to accelerate drug discovery: what are the possibilities?

Authors: Justin Boyd; Myles Fennell; Anne Carpenter
Journal: Expert Opin Drug Discov Date: 2020-03-21 Impact factor: 6.098

5. Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling.

Authors: Wenying Yan; Xingyi Liu; Yibo Wang; Shuqing Han; Fan Wang; Xin Liu; Fei Xiao; Guang Hu
Journal: Front Pharmacol Date: 2020-04-30 Impact factor: 5.810

Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry.

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

2. Exploiting machine learning for end-to-end drug discovery and development.

Review 3. Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

4. Harnessing the power of microscopy images to accelerate drug discovery: what are the possibilities?

5. Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling.

Review 6. Lysosomal dysfunction-induced autophagic stress in diabetic kidney disease.

7. Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders.

8. PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression.

Review 9. Alkaloids in Contemporary Drug Discovery to Meet Global Disease Needs.

Review 10. Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review.