期刊
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
卷 20, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/ijms20112783
关键词
deep learning; artificial intelligence; neural network; structure-based drug discovery; virtual screening; scoring function
资金
- National Research Foundation of Korea [2019R1H1A2039674, 2018K000369]
- National Research Foundation of Korea [2019R1H1A2039674, 2018K000369] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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.
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