Message Passing Neural Processes. - Ben Day; Catalina Cangea; Arian R. Jamasb; Pietro Liò (CoRR, 2020)

Utilising Graph Machine Learning within Drug Discovery and Development. - Thomas Gaudelet; Ben Day; Arian R. Jamasb; Jyothish Soman; Cristian Regep; Gertrude Liu; Jeremy B. R. Hayter; Richard Vickers; Charles Roberts; Jian Tang 005; David Roblin; Tom L. Blundell; et al. (CoRR, 2020)

SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targets. - Ali F. Alsulami; Sherine E. Thomas; Arian R. Jamasb; Christopher A. Beaudoin; Ismail Moghul; Bridget Bannerman; Liviu Copoiu; Sundeep Chaitanya Vedithi; Pedro Torres 002; Tom L. Blundell (Briefings Bioinform., 2021)

Utilizing graph machine learning within drug discovery and development. - Thomas Gaudelet; Ben Day; Arian R. Jamasb; Jyothish Soman; Cristian Regep; Gertrude Liu; Jeremy B. R. Hayter; Richard Vickers; Charles Roberts; Jian Tang 005; David Roblin; Tom L. Blundell; et al. (Briefings Bioinform., 2021)

On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction. - Edward Elson Kosasih; Joaquín Cabezas; Xavier Sumba; Piotr Bielak; Kamil Tagowski; Kelvin Idanwekhai; Benedict Aaron Tjandra; Arian Rokkum Jamasb (CoRR, 2021)

Structure-aware generation of drug-like molecules. - Pavol Drotár; Arian Rokkum Jamasb; Ben Day; Catalina Cangea; Pietro Liò (CoRR, 2021)

Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks. - Arian R. Jamasb; Ramón Viñas Torné 001; Eric Ma; Yuanqi Du; Charles Harris; Kexin Huang; Dominic Hall; Pietro Lió; Tom L. Blundell (NeurIPS, 2022)

Structure-based Drug Design with Equivariant Diffusion Models. - Arne Schneuing; Yuanqi Du; Charles Harris; Arian R. Jamasb; Ilia Igashov; Weitao Du; Tom L. Blundell; Pietro Lió; Carla Gomes 001; Max Welling; Michael M. Bronstein; Bruno E. Correia (CoRR, 2022)

GAUCHE: A Library for Gaussian Processes in Chemistry. - Ryan-Rhys Griffiths; Leo Klarner; Henry B. Moss; Aditya Ravuri; Sang Truong; Bojana Rankovic; Yuanqi Du; Arian R. Jamasb; Julius Schwartz; Austin Tripp; Gregory Kell; Anthony Bourached; et al. (CoRR, 2022)

Protein Representation Learning by Geometric Structure Pretraining. - Zuobai Zhang; Minghao Xu; Arian Rokkum Jamasb; Vijil Chenthamarakshan; Aurélie C. Lozano; Payel Das; Jian Tang 005 (ICLR, 2023)

GAUCHE: A Library for Gaussian Processes in Chemistry. - Ryan-Rhys Griffiths; Leo Klarner; Henry B. Moss; Aditya Ravuri; Sang Truong; Yuanqi Du; Samuel Stanton; Gary Tom; Bojana Rankovic; Arian Rokkum Jamasb; Aryan Deshwal; Julius Schwartz; et al. (NeurIPS, 2023)

Multi-State RNA Design with Geometric Multi-Graph Neural Networks. - Chaitanya K. Joshi; Arian R. Jamasb; Ramón Viñas 001; Charles Harris; Simon V. Mathis; Pietro Liò (CoRR, 2023)

Evaluating Representation Learning on the Protein Structure Universe. - Arian Rokkum Jamasb; Alex Morehead; Chaitanya K. Joshi; Zuobai Zhang; Kieran Didi; Simon V. Mathis; Charles Harris; Jian Tang 005; Jianlin Cheng; Pietro Lio; Tom L. Blundell (ICLR, 2024)

Structure-based drug design by denoising voxel grids. - Pedro O. Pinheiro; Arian Rokkum Jamasb; Omar Mahmood; Vishnu Sresht; Saeed Saremi (ICML, 2024)

RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design. - Rishabh Anand; Chaitanya K. Joshi; Alex Morehead; Arian R. Jamasb; Charles Harris; Simon V. Mathis; Kieran Didi; Bryan Hooi; Pietro Liò (CoRR, 2024)

Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design. - Natasa Tagasovska; Ji Won Park; Matthieu Kirchmeyer; Nathan C. Frey; Andrew Martin Watkins; Aya Abdelsalam Ismail; Arian Rokkum Jamasb; Edith Lee; Tyler Bryson; Stephen Ra; Kyunghyun Cho (CoRR, 2024)

Machine learning-aided generative molecular design. - Yuanqi Du; Arian R. Jamasb; Jeff Guo; Tianfan Fu; Charles Harris; Yingheng Wang; Chenru Duan; Pietro Liò; Philippe Schwaller; Tom L. Blundell (Nat. Mac. Intell., 2024)

Structure-based drug design with equivariant diffusion models. - Arne Schneuing; Charles Harris; Yuanqi Du; Kieran Didi; Arian Rokkum Jamasb; Ilia Igashov; Weitao Du; Carla P. Gomes; Tom L. Blundell; Pietro Lio; Max Welling; Michael M. Bronstein; et al. (Nat. Comput. Sci., 2024)

gRNAde: Geometric Deep Learning for 3D RNA inverse design. - Chaitanya K. Joshi; Arian Rokkum Jamasb; Ramón Viñas Torné 001; Charles Harris; Simon V. Mathis; Alex Morehead; Rishabh Anand; Pietro Lio (ICLR, 2025)

RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design. - Rishabh Anand; Chaitanya K. Joshi; Alex Morehead; Arian Rokkum Jamasb; Charles Harris; Simon V. Mathis; Kieran Didi; Rex Ying; Bryan Hooi; Pietro Lio (Trans. Mach. Learn. Res., 2025)