Comprehensive Survey of Recent Drug Discovery Using Deep Learning

• Published in: International journal of molecular sciences 2021-09, Vol.22 (18), p.9983
• Main Authors: Kim, Jintae, Park, Sera, Min, Dongbo, Kim, Wankyu
• Format: Article
• Language: English
• Subjects: Artificial intelligence; artificial intelligence-based drug discovery; Binding sites; Chemists; Clinical trials; Datasets; de novo drug design; Deep learning; Drug development; Drug discovery; Drugs; drug–target interaction; Ligands; Model testing; molecular representation; Molecular structure; Natural language; Neural networks; Pharmaceutical industry; Predictions; Proteins; R&D; Research & development; Review; Therapeutic targets; virtual screening
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms22189983

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.