Comprehensive Survey of Recent Drug Discovery Using Deep Learning
- 주제(키워드) artificial intelligence-based drug discovery , deep learning , drug-target interaction , virtual screening , de novo drug design , molecular representation , benchmark tool
- 주제(기타) Biochemistry & Molecular Biology; Chemistry, Multidisciplinary
- 설명문(일반) [Kim, Jintae; Park, Sera; Kim, Wankyu] KaiPharm Co Ltd, Seoul 03759, South Korea; [Min, Dongbo] Ewha Womans Univ, Dept Comp Sci & Engn, Comp Vis Lab, Seoul 03760, South Korea; [Kim, Wankyu] Ewha Womans Univ, Dept Life Sci, Syst Pharmacol Lab, Seoul 03760, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- OA유형 Green Published, gold
- 발행기관 MDPI
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000183593
- 본문언어 영어
- Published As http://dx.doi.org/10.3390/ijms22189983
- PubMed https://pubmed.ncbi.nlm.nih.gov/34576146
초록/요약
Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug-target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.
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