A Structure-Based Drug Discovery Paradigm
Top Cited Papers
Open Access
- 6 June 2019
- journal article
- review article
- Published by MDPI AG in International Journal of Molecular Sciences
- Vol. 20 (11), 2783
- https://doi.org/10.3390/ijms20112783
Abstract
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.Keywords
Funding Information
- National Research Foundation of Korea (2019R1H1A2039674)
- Ministry of Science and ICT (2018K000369)
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