我和王家祺博士、宋有义博士在Mathematics, Algorithms, Big Data and Cognitive Computing, BioMedInformatics, Information等五个期刊组织了Machine Learning Empowered Drug Screen的主题special issue，求赐稿。

药物设计是一个漫长、昂贵、困难和低效的过程。寻找有效的药物途径对于抗击未来疫情至关重要。计算机辅助药物设计（CADD）在加速发现潜在先导化合物和优化其结构以进行后续药理学试验方面起着至关重要的作用。在CADD中，机器学习被广泛用于训练模型来预测目标属性，包括其抑制力和毒性，需要机器学习方法来更好地加速药物的设计。在本期的“机器学习——驱动药物筛选”特刊中，我们将讨论使用机器学习方法进行药物筛选的各个方面。

投稿链接：https://www.mdpi.com/topics/PK0R6X64GI

Topic Information

Dear Colleagues,

Drug design is a lengthy, costly, difficult, and inefficient process in spite of advances in biotechnology and the understanding of biological systems. Finding efficient drug pathways is crucial in the fight against future outbreaks, and much effort has been devoted to it. Computer-aided drug design (CADD) plays a vital role in accelerating the discovery of potential lead compounds and the optimization of their structure for the following pharmacological tests. In CADD, machine learning is widely used to train a model to predict the target properties including their potency and toxicity. Thus, machine learning methods are required to better accelerate the design of drugs. In this Special Issue on “Machine Learning-Empowered Drug Screen”, we will discuss various aspects of drug screen using machine learning methods.

Dr. Teng Zhou
Dr. Jiaqi Wang
Dr. Youyi Song
Topic Editors

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