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Arian Rokkum Jamasb
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2020 – today
- 2024
- [j3]Yuanqi Du, Arian R. Jamasb
, Jeff Guo
Machine learning-aided generative molecular design. Nat. Mac. Intell. 6(6): 589-604 (2024) - [c5]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:
Evaluating Representation Learning on the Protein Structure Universe. ICLR 2024 - [c4]Pedro O. Pinheiro, Arian Rokkum Jamasb, Omar Mahmood, Vishnu Sresht, Saeed Saremi:
Structure-based drug design by denoising voxel grids. ICML 2024 - [i11]Rishabh Anand, Chaitanya K. Joshi, Alex Morehead, Arian R. Jamasb, Charles Harris, Simon V. Mathis, Kieran Didi, Bryan Hooi, Pietro Liò:
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design. CoRR abs/2406.13839 (2024) - [i10]Arian R. Jamasb, Alex Morehead, Chaitanya K. Joshi, Zuobai Zhang, Kieran Didi, Simon V. Mathis, Charles Harris, Jian Tang, Jianlin Cheng, Pietro Lio, Tom L. Blundell:
Evaluating representation learning on the protein structure universe. CoRR abs/2406.13864 (2024) - [i9]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:
Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design. CoRR abs/2407.21028 (2024) - 2023
- [c3]Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang:
Protein Representation Learning by Geometric Structure Pretraining. ICLR 2023 - [c2]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:
GAUCHE: A Library for Gaussian Processes in Chemistry. NeurIPS 2023 - [i8]Chaitanya K. Joshi, Arian R. Jamasb, Ramón Viñas, Charles Harris, Simon V. Mathis, Pietro Liò:
Multi-State RNA Design with Geometric Multi-Graph Neural Networks. CoRR abs/2305.14749 (2023) - 2022
- [c1]Arian R. Jamasb, Ramón Viñas Torné, Eric Ma, Yuanqi Du, Charles Harris, Kexin Huang, Dominic Hall, Pietro Lió, Tom L. Blundell:
Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks. NeurIPS 2022 - [i7]Zuobai Zhang, Minghao Xu, Arian R. Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang:
Protein Representation Learning by Geometric Structure Pretraining. CoRR abs/2203.06125 (2022) - [i6]Arne Schneuing
Structure-based Drug Design with Equivariant Diffusion Models. CoRR abs/2210.13695 (2022) - [i5]Ryan-Rhys Griffiths
GAUCHE: A Library for Gaussian Processes in Chemistry. CoRR abs/2212.04450 (2022) - 2021
- [j2]Ali F. Alsulami
SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targets. Briefings Bioinform. 22(2): 769-780 (2021) - [j1]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
Utilizing graph machine learning within drug discovery and development. Briefings Bioinform. 22(6) (2021) - [i4]Edward Elson Kosasih, Joaquín Cabezas, Xavier Sumba, Piotr Bielak, Kamil Tagowski, Kelvin Idanwekhai, Benedict Aaron Tjandra, Arian Rokkum Jamasb:
On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction. CoRR abs/2106.15529 (2021) - [i3]Pavol Drotár, Arian Rokkum Jamasb, Ben Day, Catalina Cangea, Pietro Liò:
Structure-aware generation of drug-like molecules. CoRR abs/2111.04107 (2021) - 2020
- [i2]Ben Day, Catalina Cangea, Arian R. Jamasb, Pietro Liò:
Message Passing Neural Processes. CoRR abs/2009.13895 (2020) - [i1]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:
Utilising Graph Machine Learning within Drug Discovery and Development. CoRR abs/2012.05716 (2020)
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