Research Area:  Machine Learning

Abstract:

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.

Keywords:  
Artificial intelligence-based drug discovery
deep learning
drug–target interaction
virtual screening
de novo drug design
molecular representation
benchmark tool

Author(s) Name:  Jintae Kim,Sera Park ,Dongbo Min and Wankyu Kim

Publisher name:  MDPI

DOI:  10.3390/ijms22189983

Volume Information:  Volume 22, Issue 18

Paper Link:   https://www.mdpi.com/1422-0067/22/18/9983