Work

Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review

Year: 2025

Type: review

Abstract: Predicting drug-target interactions (DTI) is a complex task. With the introduction of artificial intelligence (AI) methods such as machine learning and deep learning, AI-based DTI prediction can significantly enhance speed, reduce costs, and screen potential drug design options before conducting actual experiments. However, the application of AI methods also faces several challenges that need to be addressed. This article reviews various AI-based approaches and suggests possible future directions.

Source: npj Biomedical Innovations.

DOI: 10.1038/s44385-024-00003-9

Authors: Qian Liao, Yu Zhang, Ying Chu, Yi Ding, Zhen Liu, Xianyi Zhao, Yizheng Wang, Jie Wan, Yijie Ding, Prayag Tiwari, Quan Zou, Ke Han

Institutions: University of Electronic Science and Technology of China, Harbin University of Commerce, Harbin Institute of Technology, Halmstad University

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Drug Discovery

Field: Pharmacology, Toxicology and Pharmaceutics

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690

Funders: National Natural Science Foundation of China, Harbin University of Commerce, Högskolan i Halmstad, Natural Science Foundation of Zhejiang Province

Awards: G2204089524, G2245046682, 基于深度多核学习框架的多模态药物副作用识别方法研究, G5063593930, G7037578608, G7543040840

OpenAlex ID: W4406312972

FWCI: 4.394

Cites: 103

Cited by: 88

Related to: 10