Fuqiang Ban

According to our database, Fuqiang Ban authored at least 8 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

PROTACable Is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs.

Hazem Mslati, Francesco Gentile, Mohit Pandey, Fuqiang Ban, Artem Cherkasov

J. Chem. Inf. Model., 2024

2023

Large-Scale Virtual Screening for the Discovery of SARS-CoV-2 Papain-like Protease (PLpro) Non-covalent Inhibitors.

Olivia Garland, Anh-Tien Ton, Shoeib Moradi, Jason R. Smith, Suzana Kovacic, Kurtis Ng, Mohit Pandey, Fuqiang Ban, Jaeyong Lee, Marija Vuckovic, Liam J. Worrall, Robert N. Young, Ralph Pantophlet, Natalie C. J. Strynadka, Artem Cherkasov

J. Chem. Inf. Model., April, 2023

2019

Quantitative Structure-Price Relationship (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects.

Michael Fernández, Fuqiang Ban, Godwin Woo, Olexandr Isayev, Carl Perez, Valery Fokin, Alexander Tropsha, Artem Cherkasov

J. Chem. Inf. Model., 2019

Progressive Docking - Deep Learning Based Approach for Accelerated Virtual Screening.

Vibudh Agrawal, Francesco Gentile, Michael Hsing, Fuqiang Ban, Artem Cherkasov

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019 - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, 2019

2018

Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images.

Michael Fernández, Fuqiang Ban, Godwin Woo, Michael Hsing, Takeshi Yamazaki, Eric Leblanc, Paul S. Rennie, William J. Welch, Artem Cherkasov

J. Chem. Inf. Model., 2018

2017

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.

Fuqiang Ban, Kush Dalal, Huifang Li, Eric Leblanc, Paul S. Rennie, Artem Cherkasov

J. Chem. Inf. Model., 2017

SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines.

Tong He, Marten Heidemeyer, Fuqiang Ban, Artem Cherkasov, Martin Ester

J. Cheminformatics, 2017

2005

Modeling the Zinc Chelating Rosette Nanotube.

Fuqiang Ban, Savariraj Kingsley, Ross Johnson, Hicham Fenniri, Andriy Kovalenko

Proceedings of the 2005 International Conference on MEMS, 2005