| Utilizing graph machine learning within drug discovery and development T Gaudelet, B Day, AR Jamasb, J Soman, C Regep, G Liu, JBR Hayter, ... Briefings in bioinformatics 22 (6), bbab159, 2021 | 176 | 2021 |
| Complete Connectomic Reconstruction of Olfactory Projection Neurons in the Fly Brain AS Bates, P Schlegel, RJV Roberts, N Drummond, IFM Tamimi, ... Current Biology 30 (16), 3183-3199.e6, 2020 | 134 | 2020 |
| Functional and anatomical specificity in a higher olfactory centre S Frechter, AS Bates, S Tootoonian, MJ Dolan, J Manton, AR Jamasb, ... Elife 8, e44590, 2019 | 83 | 2019 |
| Ethoscopes: An open platform for high-throughput ethomics Q Geissmann, L Garcia Rodriguez, EJ Beckwith, AS French, AR Jamasb, ... PLoS biology 15 (10), e2003026, 2017 | 73 | 2017 |
| Protein representation learning by geometric structure pretraining Z Zhang, M Xu, A Jamasb, V Chenthamarakshan, A Lozano, P Das, ... ICLR 2023, 2022 | 69* | 2022 |
| Structure-based drug design with equivariant diffusion models A Schneuing, Y Du, C Harris, A Jamasb, I Igashov, W Du, T Blundell, P Lió, ... arXiv preprint arXiv:2210.13695, 2022 | 51* | 2022 |
| Graphein-a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks AR Jamasb, RV Torné, EJ Ma, Y Du, C Harris, K Huang, D Hall, P Lio, ... NeurIPS 2022, 2022 | 42* | 2022 |
| GAUCHE: A library for Gaussian processes in chemistry RR Griffiths, L Klarner, HB Moss, A Ravuri, S Truong, B Rankovic, Y Du, ... NeurIPS 2023, 2022 | 40* | 2022 |
| Data-driven discovery of molecular photoswitches with multioutput Gaussian processes RR Griffiths, JL Greenfield, AR Thawani, AR Jamasb, HB Moss, ... Chemical Science 13 (45), 13541-13551, 2022 | 31* | 2022 |
| SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targets AF Alsulami, SE Thomas, AR Jamasb, CA Beaudoin, I Moghul, ... Briefings in bioinformatics 22 (2), 769-780, 2021 | 29 | 2021 |
| Predicted structural mimicry of spike receptor-binding motifs from highly pathogenic human coronaviruses CA Beaudoin, AR Jamasb, AF Alsulami, L Copoiu, AJ van Tonder, S Hala, ... Computational and Structural Biotechnology Journal 19, 3938-3953, 2021 | 27 | 2021 |
| Structure-aware generation of drug-like molecules P Drotár, AR Jamasb, B Day, C Cangea, P Liò arXiv preprint arXiv:2111.04107, 2021 | 16 | 2021 |
| Deep learning for protein–protein interaction site prediction AR Jamasb, B Day, C Cangea, P Liò, TL Blundell Proteomics Data Analysis, 263-288, 2021 | 16* | 2021 |
| Message Passing Neural Processes C Cangea, B Day, AR Jamasb, P Lio ICLR 2022 Workshop on Geometrical and Topological Representation Learning, 2022 | 8* | 2022 |
| On graph neural network ensembles for large-scale molecular property prediction EE Kosasih, J Cabezas, X Sumba, P Bielak, K Tagowski, K Idanwekhai, ... arXiv preprint arXiv:2106.15529, 2021 | 5 | 2021 |
| Decoding surface fingerprints for protein-ligand interactions I Igashov, AR Jamasb, A Sadek, F Sverrisson, A Schneuing, P Lio, ... bioRxiv, 2022.04. 26.489341, 2022 | 4 | 2022 |
| Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models? C Harris, K Didi, AR Jamasb, CK Joshi, SV Mathis, P Lio, T Blundell arXiv preprint arXiv:2308.07413, 2023 | | 2023 |
| Multi-State RNA Design with Geometric Multi-Graph Neural Networks CK Joshi, AR Jamasb, R Viñas, C Harris, S Mathis, P Liò arXiv preprint arXiv:2305.14749, 2023 | | 2023 |
| Flexible Small-Molecule Design and Optimization with Equivariant Diffusion Models C Harris, K Didi, A Schneuing, Y Du, AR Jamasb, MM Bronstein, ... ICLR 2023-Machine Learning for Drug Discovery workshop, 2023 | | 2023 |
| GrapHiC-A Python library for creating bespoke graph datasets from Hi-C & Multi-omics data D Hall, AR Jamasb, M Rozenwald, P Lió | | |
Tom L BlundellEmeritus Professor of Biochemistry, University of CambridgeVerified email at cam.ac.uk
