DLIGAND2: an improved knowledge-based energy function for protein–ligand interactions using the distance-scaled, finite, ideal-gas reference state-Reference-Cited by-同舟云学术

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DLIGAND2: an improved knowledge-based energy function for protein–ligand interactions using the distance-scaled, finite, ideal-gas reference state

Published:2019-08-07 Issue:1 Volume:11 Page:
ISSN:1758-2946
Container-title:Journal of Cheminformatics
language:en
Short-container-title:J Cheminform

Author:

Chen Pin^ORCID,Ke Yaobin^ORCID,Lu Yutong^ORCID,Du Yunfei^ORCID,Li Jiahui^ORCID,Yan Hui^ORCID,Zhao Huiying^ORCID,Zhou Yaoqi^ORCID,Yang Yuedong^ORCID

Funder

GD Frontier & Key Techn, Innovation Program

National Natural Science Foundation of China

Australian Research Council

National Health and Medical Research Council

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Computer Graphics and Computer-Aided Design,Physical and Theoretical Chemistry,Computer Science Applications

Link

http://link.springer.com/content/pdf/10.1186/s13321-019-0373-4.pdf

Reference60 articles.

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2. Valasani KR, Vangavaragu JR, Day VW, Yan SS (2014) Structure based design, synthesis, pharmacophore modeling, virtual screening, and molecular docking studies for identification of novel cyclophilin D inhibitors. J Chem Inf Model 54:902–912

3. Singh AN, Baruah MM, Sharma N (2017) Structure based docking studies towards exploring potential anti-androgen activity of selected phytochemicals against Prostate Cancer. Sci Rep 7:1955

4. Wang Z, Sun H, Yao X, Li D, Xu L, Li Y, Tian S, Hou T (2016) Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys 18:12964–12975

5. Li Y, Han L, Liu Z, Wang R (2014) Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results. J Chem Inf Model 54:1717–1736

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