APObind: A Dataset of Ligand Unbound Protein Conformations for Machine Learning Applications in De Novo Drug Design. - Rishal Aggarwal; Akash Gupta 002; U. Deva Priyakumar (CoRR, 2021)

GNINA 1.0: molecular docking with deep learning. - Andrew T. McNutt; Paul G. Francoeur; Rishal Aggarwal; Tomohide Masuda; Rocco Meli; Matthew Ragoza; Jocelyn Sunseri; David Ryan Koes (J. Cheminformatics, 2021)

DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks. - Rishal Aggarwal; Akash Gupta 002; Vineeth R. Chelur; C. V. Jawahar; U. Deva Priyakumar (J. Chem. Inf. Model., 2022)

MolGPT: Molecular Generation Using a Transformer-Decoder Model. - Viraj Bagal; Rishal Aggarwal; P. K. Vinod; U. Deva Priyakumar (J. Chem. Inf. Model., 2022)

BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening. - Michael Brocidiacono; Paul G. Francoeur; Rishal Aggarwal; Konstantin I. Popov; David Ryan Koes; Alexander Tropsha (J. Chem. Inf. Model., 2024)

BoltzNCE: Learning Likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimation. - Rishal Aggarwal; Jacky Chen; Nicholas M. Boffi; David Ryan Koes (CoRR, 2025)

GNINA 1.3: the next increment in molecular docking with deep learning. - Andrew T. McNutt; Yanjing Li; Rocco Meli; Rishal Aggarwal; David Ryan Koes (J. Cheminformatics, 2025)