Spectrum of deep learning algorithms in drug discovery
- 1 September 2020
- journal article
- research article
- Published by Wiley in Chemical Biology & Drug Design
- Vol. 96 (3), 886-901
- https://doi.org/10.1111/cbdd.13674
Abstract
Deep learning (DL) algorithms are a subset of machine learning algorithms with the aim of modeling complex mapping between a set of elements and their classes. In parallel to the advance in revealing the molecular bases of diseases, a notable innovation has been undertaken to apply DL in data/libraries management, reaction optimizations, differentiating uncertainties, molecule constructions, creating metrics from qualitative results, and prediction of structures or interactions. From source identification to lead discovery and medicinal chemistry of the drug candidate, drug delivery, and modification, the challenges can be subjected to artificial intelligence algorithms to aid in the generation and interpretation of data. Discovery and design approach, both demand automation, large data management and data fusion by the advance in high-throughput mode. The application of DL can accelerate the exploration of drug mechanisms, finding novel indications for existing drugs (drug repositioning), drug development, and preclinical and clinical studies. The impact of DL in the workflow of drug discovery, design, and their complementary tools are highlighted in this review. Additionally, the type of DL algorithms used for this purpose, and their pros and cons along with the dominant directions of future research are presented.Keywords
This publication has 100 references indexed in Scilit:
- Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead OptimizationJournal of Medicinal Chemistry, 2012
- PKMiner: a database for exploring type II polyketide synthasesBMC Microbiology, 2012
- A Data-Driven Predictive Approach for Drug Delivery Using Machine Learning TechniquesPLOS ONE, 2012
- AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environmentJournal of Cheminformatics, 2011
- NRPSpredictor2—a web server for predicting NRPS adenylation domain specificityNucleic Acids Research, 2011
- Machine learning in drug discovery and developmentDrug Development Research, 2010
- The 'big pharma' dilemma: develop new drugs or promote existing ones?Nature Reviews Drug Discovery, 2009
- Data mining PubChem using a support vector machine with the Signature molecular descriptor: Classification of factor XIa inhibitorsJournal of Molecular Graphics and Modelling, 2008
- Molecular Docking and High-Throughput Screening for Novel Inhibitors of Protein Tyrosine Phosphatase-1BJournal of Medicinal Chemistry, 2002
- Artificial Neural Networks As a Novel Approach to Integrated Pharmacokinetic—Pharmacodynamic AnalysisJournal of Pharmaceutical Sciences, 1996