ScaffComb: A Phenotype‐Based Framework for Drug Combination Virtual Screening in Large‐Scale Chemical Datasets

Advanced Science

Fengling Chen¹  Jiangyang Zeng²  Juntao Gao²  Zhaofeng Ye²  Michael Q. Zhang² 

[1] Center for Stem Cell Biology and Regenerative Medicine MOE Key Laboratory of Bioinformatics Tsinghua University Beijing 100084 China;MOE Key Laboratory of Bioinformatics Bioinformatics Division Center for Synthetic and Systems Biology BNRist Department of Automation Tsinghua University Beijing 100084 China;

关键词: deep learning;    drug combination;    scaffold;    virtual screening;   

DOI  :  10.1002/advs.202102092

来源: DOAJ

【 摘 要 】

Abstract Combinational therapy is used for a long time in cancer treatment to overcome drug resistance related to monotherapy. Increased pharmacological data and the rapid development of deep learning methods have enabled the construction of models to predict and screen drug pairs. However, the size of drug libraries is restricted to hundreds to thousands of compounds. The ScaffComb framework, which aims to bridge the gaps in the virtual screening of drug combinations in large‐scale databases, is proposed here. Inspired by phenotype‐based drug design, ScaffComb integrates phenotypic information into molecular scaffolds, which can be used to screen the drug library and identify potent drug combinations. First, ScaffComb is validated using the US food and drug administration dataset and known drug combinations are successfully reidentified. Then, ScaffComb is applied to screen the ZINC and ChEMBL databases, which yield novel drug combinations and reveal an ability to discover new synergistic mechanisms. To our knowledge, ScaffComb is the first method to use phenotype‐based virtual screening of drug combinations in large‐scale chemical datasets.

【 授权许可】

Unknown