Journal
FUTURE MEDICINAL CHEMISTRY
Volume 10, Issue 21, Pages 2557-2567Publisher
FUTURE SCI LTD
DOI: 10.4155/fmc-2018-0314
Keywords
artificial intelligence; artificial neural networks; convolutional neural networks; deep learning; drug discovery; machine learning; multitask learning; virtual screening
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Funding
- NIH [R01AG056614]
- NATIONAL INSTITUTE ON AGING [R01AG056614] Funding Source: NIH RePORTER
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Current drug development is still costly and slow given tremendous technological advancements in drug discovery and medicinal chemistry. Using machine learning (ML) to virtually screen compound libraries promises to fix this for generating drug leads more efficiently and accurately. Herein, we explain the broad basics and integration of both virtual screening (VS) and ML. We then discuss artificial neural networks (ANNs) and their usage for VS. The ANN is emerging as the dominant classifier for ML in general, and has proven its utility for both structure-based and ligand-based VS. Techniques such as dropout, multitask learning and convolution improve the performance of ANNs and enable them to take on chemical meaning when learning about the drug-target-binding activity of compounds.
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