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我和王家祺博士、宋有義博士在Mathematics, Algorithms, Big Data and Cognitive Computing, BioMedInformatics, Information等五個期刊組織了Machine Learning Empowered Drug Screen的主題special issue，求賜稿。

藥物設(shè)計是一個漫長、昂貴、困難和低效的過程。尋找有效的藥物途徑對于抗擊未來疫情至關(guān)重要。計算機輔助藥物設(shè)計（CADD）在加速發(fā)現(xiàn)潛在先導化合物和優(yōu)化其結(jié)構(gòu)以進行后續(xù)藥理學試驗方面起著至關(guān)重要的作用。在CADD中，機器學習被廣泛用于訓練模型來預測目標屬性，包括其抑制力和毒性，需要機器學習方法來更好地加速藥物的設(shè)計。在本期的“機器學習——驅(qū)動藥物篩選”特刊中，我們將討論使用機器學習方法進行藥物篩選的各個方面。

投稿鏈接：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

Keywords