Journal: Current Topics in Medicinal Chemistry
Guest editor(s):Dr. Liangxu Xie
Co-Guest Editor(s): Xiaojun Xu

Introduction

In the age of artificial intelligence (AI), we have witnessed a significant boom in AI techniques for drug discovery. AI techniques are increasingly integrated and accelerating the drug discovery process. These developments have not only attracted the attention of academia and industry but also raised important questions regarding the selection of the most suitable AI methods. The thematic issue aims to showcase the latest advancements, methodologies, and applications of artificial intelligence in the field of drug discovery. We would like to take this opportunity to thoroughly review AI methods and their applications. The thematic issue will encompass a broad range of topics related to AI-driven approaches in drug discovery. This includes, but is not limited to, applications of deep learning in candidate screening, target identification and structure prediction exemplified by AlphaFold2, hybrid AI and molecular modeling, drug repurposing, prediction and optimization of drug properties, integration of multi-omics data, and de-novo drug generation, etc. Authors are encouraged to submit reviews and perspectives that present novel methodologies, experimental findings, new developments in AI, or critical insights into the application of AI in drug discovery.

Keywords

artificial intelligence, deep learning, de novo drug design, drug-target interaction, quantitative structure-activity relationships, big data, bioinformatics, structure prediction

Sub-topics

Machine learning and deep learning for drug target identification and validation.
Predictive modeling for protein and RNA structure prediction.
Integration of multi-omics data and AI methods for drug design.
AI-driven de novo drug design and lead optimization.
Hybrid AI algorithms and molecular modeling techniques
Explainability techniques in AI-based drug discovery.