Arian Rokkum Jamasb

According to our database, Arian Rokkum Jamasb authored at least 17 papers between 2020 and 2024.

Collaborative distances:

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

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

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

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

Structure-based drug design by denoising voxel grids.

Pedro O. Pinheiro, Arian Rokkum Jamasb, Omar Mahmood, Vishnu Sresht, Saeed Saremi

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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, Jianlin Cheng, Pietro Lio, Tom L. Blundell

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Multi-State RNA Design with Geometric Multi-Graph Neural Networks.

Chaitanya K. Joshi, Arian R. Jamasb, Ramón Viñas, Charles Harris, Simon V. Mathis, Pietro Liò

CoRR, 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, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Peter Dürholt, Saudamini Chaurasia, Ji Won Park, Felix Strieth-Kalthoff, Alpha A. Lee, Bingqing Cheng, Alán Aspuru-Guzik, Philippe Schwaller, Jian Tang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Protein Representation Learning by Geometric Structure Pretraining.

Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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, Alex Chan, Jacob Moss, Chengzhi Guo, Alpha A. Lee, Philippe Schwaller, Jian Tang

CoRR, 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, Max Welling, Michael M. Bronstein, Bruno E. Correia

CoRR, 2022

Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks.

Arian R. Jamasb, Ramón Viñas Torné, Eric Ma, Yuanqi Du, Charles Harris, Kexin Huang, Dominic Hall, Pietro Lió, Tom L. Blundell

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Structure-aware generation of drug-like molecules.

Pavol Drotár, Arian Rokkum Jamasb, Ben Day, Catalina Cangea, Pietro Liò

CoRR, 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

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, David Roblin, Tom L. Blundell, Michael M. Bronstein, Jake P. Taylor-King

Briefings Bioinform., 2021

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, Tom L. Blundell

Briefings Bioinform., 2021

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, David Roblin, Tom L. Blundell, Michael M. Bronstein, Jake P. Taylor-King

CoRR, 2020

Message Passing Neural Processes.

Ben Day, Catalina Cangea, Arian R. Jamasb, Pietro Liò

CoRR, 2020