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Fuqiang Ban
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2020 – today
- 2024
- [j6]Hazem Mslati
, Francesco Gentile, Mohit Pandey, Fuqiang Ban, Artem Cherkasov
PROTACable Is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs. J. Chem. Inf. Model. 64(8): 3034-3046 (2024) - 2023
- [j5]Olivia Garland
Large-Scale Virtual Screening for the Discovery of SARS-CoV-2 Papain-like Protease (PLpro) Non-covalent Inhibitors. J. Chem. Inf. Model. 63(7): 2158-2169 (2023)
2010 – 2019
- 2019
- [j4]Michael Fernández
Quantitative Structure-Price Relationship (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects. J. Chem. Inf. Model. 59(4): 1306-1313 (2019) - [c2]Vibudh Agrawal
Progressive Docking - Deep Learning Based Approach for Accelerated Virtual Screening. ICANN (Workshop) 2019: 737-740 - 2018
- [j3]Michael Fernández
Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images. J. Chem. Inf. Model. 58(8): 1533-1543 (2018) - 2017
- [j2]Tong He, Marten Heidemeyer, Fuqiang Ban, Artem Cherkasov, Martin Ester
SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines. J. Cheminformatics 9(1): 24:1-24:14 (2017) - [j1]Fuqiang Ban, Kush Dalal, Huifang Li, Eric Leblanc, Paul S. Rennie, Artem Cherkasov
Best Practices of Computer-Aided Drug Discovery: Lessons Learned from the Development of a Preclinical Candidate for Prostate Cancer with a New Mechanism of Action. J. Chem. Inf. Model. 57(5): 1018-1028 (2017)
2000 – 2009
- 2005
- [c1]Fuqiang Ban, Savariraj Kingsley, Ross Johnson, Hicham Fenniri, Andriy Kovalenko:
Modeling the Zinc Chelating Rosette Nanotube. ICMENS 2005: 379-382
Coauthor Index
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