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Arian Rokkum Jamasb

Alumni

Publications

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 Leon Blundell
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
GAUCHE: A Library for Gaussian Processes in Chemistry
Ryan-Rhys Griffiths
Leo Klarner
Henry Moss
Aditya Ravuri
Sang T. Truong
Bojana Rankovic
Samuel Don Stanton
Yuanqi Du
Arian Rokkum Jamasb
Gary Tom
Julius Schwartz
Austin Tripp
Aryan Deshwal
Gregory Kell
Anthony Bourached
Alex James Chan
Jacob Moss
Chengzhi Guo
Simon Frieder
Alpha Lee … (see 8 more)
Philippe Schwaller
Jian Tang
Johannes P. Dürholt
Saudamini Chaurasia
Ji Won Park
Felix Strieth-Kalthoff
Bingqing Cheng
Alan Aspuru-Guzik
We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine… (see more) learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to chemical representations however is nontrivial, necessitating kernels defined over structured inputs such as graphs, strings and bit vectors. By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry. Motivated by scenarios frequently encountered in experimental chemistry, we showcase applications for GAUCHE in molecular discovery and chemical reaction optimisation. The codebase is made available at https://github.com/leojklarner/gauche
openreview.net
Protein Representation Learning by Geometric Structure Pretraining
Zuobai Zhang
Minghao Xu
Arian Rokkum Jamasb
Vijil Chenthamarakshan
Aurelie Lozano
Payel Das
Jian Tang
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Ex… (see more)isting approaches usually pretrain protein language models on a large number of unlabeled amino acid sequences and then finetune the models with some labeled data in downstream tasks. Despite the effectiveness of sequence-based approaches, the power of pretraining on known protein structures, which are available in smaller numbers only, has not been explored for protein property prediction, though protein structures are known to be determinants of protein function. In this paper, we propose to pretrain protein representations according to their 3D structures. We first present a simple yet effective encoder to learn the geometric features of a protein. We pretrain the protein graph encoder by leveraging multiview contrastive learning and different self-prediction tasks. Experimental results on both function prediction and fold classification tasks show that our proposed pretraining methods outperform or are on par with the state-of-the-art sequence-based methods, while using much less pretraining data. Our implementation is available at https://github.com/DeepGraphLearning/GearNet.
2023-02-01
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net