I'm fascinated by how we can develop and better apply technologies to understand biological systems. So far this has taken me on a journey through classical biochemistry, scanning whole mouse brains to better understand processes of aging, the neurobiology of sleep & sexual behaviour in animals, deep into the the circuitry of the fruit fly brain to unpick the wiring of innate behaviours and learning & memory, and, finally, to the circus that is deep learning to improve drug discovery. Download my CV.
Deep learning for structural biology and drug discovery. Supervisors: Prof Sir Tom Blundell FRS & Prof Pietro Lio
Computational Biology Group , Artificial Intelligence Group
Department of Computer Science & Technology
Structural Biology & Biocomputing Group
Department of Biochemistry
University of Cambridge
2018-2022
1st Class Honours
Dissertation: 3D Imaging of the Connectome. Supervisor: Prof Stephen Brickley. Tutor: Prof Anne Dell.
[2022-] |
Senior Machine Learning Scientist Prescient Design |
[2022] | Visiting Researcher EPFL |
Supervisor: Prof Bruno Correia |
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[2021] | Visiting Researcher MILA |
Supervisor: Prof Jian Tang |
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[2021] |
AI Resident X - The Moonshot Factory (formerly Google X) |
[2020-2021] |
ML Consultant Relation Therapeutics |
[2017-2018] | Graduate Research Assistant Drosophila Connectomics Group |
Supervisor: Dr Greg Jefferis |
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[2016-2017] | Undergraduate Research Assistant Gilestro Lab |
Supervisor: Dr Giorgio Gilestro |
A more up-to-date list may be found on my Google Scholar page
[2024] |
Antibody DomainBed: Out-of-Distribution Generalization in
Therapeutic Protein Design
Nataša 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
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[2022] |
Structure-based Drug Design with Equivariant Diffusion Models.
Arne Schneuing, Yuanqi Du, Charles Harris, Arian R. Jamasb, Ilia Igashov,
Weitao Du,
Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bronstein, Bruno Correia.
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[2021] |
On Graph Neural Network Ensembles for Large-Scale Molecular
Property Prediction
Edward E. Kosasih, Joaquin Cabezas, Xavier Sumba, Piotr Bielak, Kamil Tagowski,
Kelvin Idanwekhai, Benedict A. Tjandra, Arian R. Jamasb.
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[2021] |
Deep Learning for Protein-Protein Interaction Site Prediction
Arian R. Jamasb, Ben Day, Cătălina Cangea, Pietro Liò, Tom L. Blundell.
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[2024] |
Ensemble Guidance: Towards Generative 3D SBDD in Bioactive Chemical Spaces
Charlie Harris, Arian R. Jamasb, Pietro Lio, Tom L. Blundell
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[2024] |
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design
Rishabh Anand, Chaitanya K. Joshi, Alex Morehead, Arian R. Jamasb, Charlie
Harris, Simon V. Mathis, Kieran Didi, Bryan Hooi, Pietro Lio
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[2024] |
Structure-based drug design by denoising voxel grids
Pedro O. Pinheiro, Arian R. Jamasb, Omar Mahmood, Vishnu Sresht, Saeed Saremi
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[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
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[2024] |
Evaluating Representation Learning on the Protein Structure
Universe
Arian R. Jamasb*, Alex Morehead*, Chaitanya K. Joshi*, Zuobai Zhang*, Kieran
Didi, Simon V Mathis, Charles Harris, Jian Tang, Jianlin Cheng, Pietro Lio, Tom Leon
Blundell
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[2023] |
Benchmarking Generated Poses: How Rational is Structure-based
Drug Design with Generative Models?
Charlie Harris, Kieran Didi, Arian R. Jamasb, Chaitanya Joshi, Simon Mathis,
Pietro Lio, Tom L. Blundell.
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[2023] |
GAUCHE: A Library for Gaussian Processes in Chemistry
Ryan-Rhys Griffiths*, Leo Klarner*, Henry Moss*, Aditya Ravuri*, Sang T. Truong*,
Bojana
Rankovic*, Yuanqi Du*, Arian R. Jamasb*, Aryan Deshwal, Julius Schwartz, Austin
Tripp, Gregory
Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes
Durholt, Saudamini Chaurasia, Felix Strieth-Kalthoff, Alpha A. Lee,
Bingqing Cheng, Alan Aspuru-Guzik, Philippe Schwaller, Jian Tang
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[2023] |
Multi-State RNA Design with Geometric Multi-Graph Neural
Networks
Chaitanya K Joshi, Arian R. Jamasb, Ramon Viñas, Charles Harris, Simon Mathis,
Pietro Liò
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[2023] |
Flexible Small-Molecule Design and Optimization with Equivariant
Diffusion Models
Charlie Harris, Kieran Didi, Arne Schneuing, Yuanqi Du, Arian R. Jamasb, Michael
Bronstein, Bruno Correia, Pietro Lio, Tom L. Blundell.
ICLR-W MLDD |
[2023] |
Protein Representation Learning by Geometric Structure
Pretraining
Zuobai Zhang, Minghao Xu, Arian R. Jamasb, Vijil Chenthamarakshan, Aurelie
Lozano,
Payel Das, Jian Tang.
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[2022] |
Graphein - a Python Library for Geometric Deep Learning and
Network Analysis on Biomolecular Structures and Interaction Networks.
Arian R. Jamasb, Ramon Vinas Torne, Eric J. Ma, Yuanqi Du, Kexin Huang,
Dominic Hall, Pietro Liò, Tom L. Blundell.
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[2022] |
Data-driven Discovery of Molecular Photoswitches with
Multioutput Gaussian processes
Ryan-Rhys Griffiths, Jake L Greenfield, Aditya R Thawani, Arian R. Jamasb,
Henry B Moss, Anthony Bourached, Penelope Jones, William McCorkindale, Alexander A
Aldrick, Matthew J Fuchter.
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[2022] |
Decoding Surface Fingerprints for Protein-Ligand Interactions
Ilia Igashov, Arian R. Jamasb, Ahmed Sadek, Freyr Sverrisson, Arne Schneuing,
Pietro Lio, Tom L Blundell, Michael Bronstein, Bruno F. Correia.
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[2022] |
Message Passing Neural Processes
Ben Day, Cătălina Cangea, Arian R. Jamasb, Pietro Liò.
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[2021] |
Structure-aware Generation of Drug-like Molecules
Pavol Drotár, Arian R. Jamasb, Ben Day, Cătălina Cangea, Pietro Liò
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[2021] |
Predicted Structural Mimicry of Spike Receptor-Binding Motifs
From Highly Pathogenic Human Coronaviruses
Christopher A. Beaudoin, Arian R. Jamasb, Ali F. Alsulami, Liviu Copoiu,
Andries J. van Tonder, Sharif Hala, Bridget P. Bannerman, Sherine E. Thomas, Sundeep C.
Vedithi, Pedro H. M. Torres, Tom L. Blundell.
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[2021] |
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.
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[2021] |
SARS-CoV-2 3D database: Understanding the Coronavirus Proteome
and Evaluating Possible Drug Targets
Ali Alsulami*, Sherine Thomas*, Arian R. Jamasb*, Christopher Beaudoin*,
Ismail Moghul*, Bridget Bannerman, Liviu Copoiu, Sundeep Vedithi*, Pedro Torres*, Tom
Blundell.
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[2020] |
Complete Connectomic Reconstruction of Olfactory Projection
Neurons in the Fly Brain.
Alexander S. Bates*, Philipp Schlegel*, Ruairí J. V. Roberts, Nikolas Drummond,
Imaan F. M. Tamimi, Robert G. Turnbull, Xincheng Zhao, Elizabeth C. Marin, Patricia D.
Popovici, Serene Dhawan, Arian R. Jamasb, Alexandre Javier, Feng Li, Gerald M.
Rubin, Scott Waddell, Davi D. Bock, Marta Costa, Gregory S. X. E. Jefferis.
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[2019] |
Functional and Anatomical Specificity in a Higher Olfactory
Centre.
Shahar Frechter, Alexander S. Bates, Sina Tootoonian, Michael-John Dolan, James D.
Manton, Arian R. Jamasb, Johannes Kohl, Davi Bock, Gregory S.X.E. Jefferis.
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[2017] |
Ethoscopes: An open platform for high-throughput ethomics.
Quentin Geissmann, Luis Garcia Rodriguez, Esteban J. Beckwith, Alice S. French,
Arian R. Jamasb, Giorgio F. Gilestro.
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I sometimes make music . Sometimes I play two records at once .
Feel free to reach out at arian [a] jamasb.io or follow me on Twitter GitHub or LinkedIn