Explainable Artificial Intelligence in Drug Discovery and Development
摘要截稿:
全文截稿: 2024-12-15
影响因子: 6.125
期刊难度:
CCF分类: C类
中科院JCR分区:
• 大类 : 计算机科学 - 2区
• 小类 : 计算机:理论方法 - 1区
Overview
Motivation and Scope
'Artificial Intelligence' (AI) has recently revolutionized the field of drug discovery and development, achieving breakthroughs in areas such as molecular design, chemical synthesis planning, protein structure prediction, and macromolecular target identification. Despite various computational methods proposed to address practical challenges, the complexity of these algorithms often results in limited explainability of the models, hindering our ability to understand and explain their underlying mechanisms. Given the rapid advancement of AI in drug discovery and related fields, there is an increasing demand for methods that help us understand and interpret the underlying models. Consequently, proposing 'Explainable Artificial Intelligence' (XAI) methods to address the challenge posed by the lack of explainability in deep learning models and enhancing human reasoning and decision-making capabilities have become imperative.
This special issue aims to gather papers that focus on integrating and applying advanced XAI algorithms to address the most fundamental questions in drug discovery and development, including drug repositioning, potential drug target identification, and small drug molecule target interaction and binding affinity prediction, etc. We expect the articles covering this special issue can effectively promote the drug discovery in methodology and meanwhile provide interesting insights or new biological observations.
The topics of this special issue include but not limited to:
Prediction of drug properties with XAI
Explaining drug-drug/target interaction through XAI
Development of explainable large language models for drug discovery
XAI for drug and target feature representation
XAI for ab initio drug design
XAI for virtual screening drugs
Balachandran ManavalanSungkyunkwan University, South Koreabala2022@skku.edu
Xiucai YeUniversity of Tsukuba, Japan yexiucai@cs.tsukuba.ac.jp
Dariusz MrozekSilesian University of Technology, PolandDariusz.Mrozek@polsl.pl
Important Dates
Submission portal opens: March 20, 2024
Deadline for paper submission: Dec. 15, 2024
Latest acceptance deadline for all papers: March 1, 2025