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Ligand-based_SARS-Cov-2_v5-preprint_zdzhu_20200310.pdf (1.18 MB)

D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

submitted on 10.03.2020, 02:29 and posted on 10.03.2020, 11:35 by Zhengdan Zhu, Xiaoyu Wang, Yanqing Yang, Xinben Zhang, Kaijie Mu, Yulong Shi, Cheng Peng, Zhijian Xu, weiliang zhu

Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at


Email Address of Submitting Author


Shanghai Institute of Materia Medica, Chinese Academy of Sciences



ORCID For Submitting Author


Declaration of Conflict of Interest



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