Arian
Jamasb

# Hi! I'm Arian, a PhD student in artificial intelligence and structural biology at the University of Cambridge

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.

# Education

PhD


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

Department of Life Sciences

Imperial College London


2014-2017

BSc. Biochemistry

1st Class Honours

Dissertation: 3D Imaging of the Connectome. Supervisor: Prof Stephen Brickley. Tutor: Prof Anne Dell.

# Research Experience

[2022-]

Senior Machine Learning Scientist Prescient Design

[2022] Visiting Researcher EPFL

Supervisor: Prof Bruno Correia

[2021] Visiting Researcher MILA

Supervisor: Prof Jian Tang

[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

[2016-2017] Undergraduate Research Assistant Gilestro Lab
Supervisor: Dr Giorgio Gilestro

# Publications

A more up-to-date list may be found on my Google Scholar page

## Pre-Prints

[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.
arXiv

[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.
arXiv

## Book Chapters

[2021]

Deep Learning for Protein–Protein Interaction Site Prediction Arian R. Jamasb, Ben Day, Cătălina Cangea, Pietro Liò, Tom L. Blundell.
Proteomics Data Analysis. Methods in Molecular Biology.


## Peer-reviewed

[2024]

Structure-based drug design by denoising voxel grids Pedro O. Pinheiro, Arian R. Jamasb, Omar Mahmood, Vishnu Sresht, Saeed Saremi
ICML

[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
Nature Machine Intelligence (in press)

[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
ICLR

[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.
NeurIPS-W MLSB (Oral)

[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
NeurIPS 2023, NeurIPS-W AI4Science, ELLIS-W ML4Molecules (Oral)

[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
ICML-W Computational Biology

[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.
ICLR

[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.
NeurIPS

[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.
Chemical Science

[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.
ICLR-W MLDD

[2022]

Message Passing Neural Processes Ben Day, Cătălina Cangea, Arian R. Jamasb, Pietro Liò.
ICLR 2022 Workshop on Geometrical and Topological Representation Learning

[2021]

Structure-aware Generation of Drug-like Molecules Pavol Drotár, Arian R. Jamasb, Ben Day, Cătălina Cangea, Pietro Liò
arXiv:2111.04107 [Oral Presentation at NeurIPS-W MLSB]

[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.
Computational and Structural Biotechnology Journal

[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.
Briefings in Bioinformatics

[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.
Briefings in Bioinformatics

[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.
Current Biology

[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.
eLife

[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.
PLOS Biology

# Misc

I sometimes make music. Sometimes I play two records at once.

# Contact

Feel free to reach out at arian [a] jamasb.io or follow me on Twitter GitHub or LinkedIn