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A Structure-Based Drug Discovery Paradigm

Department of Molecular Science and Technology, Ajou University, Suwon 16499, Korea
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Int. J. Mol. Sci. 2019, 20(11), 2783; https://doi.org/10.3390/ijms20112783
Received: 10 May 2019 / Revised: 31 May 2019 / Accepted: 4 June 2019 / Published: 6 June 2019
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design)
Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery. View Full-Text
Keywords: deep learning; artificial intelligence; neural network; structure-based drug discovery; virtual screening; scoring function deep learning; artificial intelligence; neural network; structure-based drug discovery; virtual screening; scoring function
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MDPI and ACS Style

Batool, M.; Ahmad, B.; Choi, S. A Structure-Based Drug Discovery Paradigm. Int. J. Mol. Sci. 2019, 20, 2783.

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