步伐
可解释性
计算机科学
药物发现
数据科学
资源(消歧)
药物开发
领域(数学分析)
机制(生物学)
人工智能
风险分析(工程)
药品
医学
生物信息学
计算机网络
数学分析
哲学
数学
大地测量学
认识论
精神科
生物
地理
作者
Zhaohui Yang,Caiqi Liu,Mujiexin Liu,Tianyuan Liu,Hao Lin,Huang Cheng-bing,Ning Lin
摘要
Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.
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