Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set. - Eelke B. Lenselink; Niels ten Dijke; Brandon J. Bongers; George Papadatos; Herman W. T. van Vlijmen; Wojtek Kowalczyk; Adriaan P. IJzerman; Gerard J. P. van Westen (J. Cheminformatics, 2017)

Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors. - Lindsey Burggraaff; Eelke B. Lenselink; Willem Jespers; Jesper E. van Engelen; Brandon J. Bongers; Marina Gorostiola González; Rongfang Liu; Holger H. Hoos; Herman W. T. van Vlijmen; Adriaan P. IJzerman; Gerard J. P. van Westen (J. Chem. Inf. Model., 2020)

Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening. - Alzbeta Türková; Brandon J. Bongers; Ulf Norinder; Orsolya Ungvári; Virág Székely; Andrey Tarnovskiy; Gergely Szakács; Csilla Özvegy-Laczka; Gerard J. P. van Westen; Barbara Zdrazil (J. Chem. Inf. Model., 2022)

Papyrus: a large-scale curated dataset aimed at bioactivity predictions. - Olivier J. M. Béquignon; Brandon J. Bongers; Willem Jespers; Adriaan P. IJzerman; B. van der Water; Gerard J. P. van Westen (J. Cheminformatics, 2023)

Proteochemometric Modeling Identifies Chemically Diverse Norepinephrine Transporter Inhibitors. - Brandon J. Bongers; Huub J. Sijben; Peter B. R. Hartog; Andrey Tarnovskiy; Adriaan P. IJzerman; Laura H. Heitman; Gerard J. P. van Westen (J. Chem. Inf. Model., 2023)