Jian Tang

Biography

Jian Tang is an Associate professor at HEC's Department of Decision Sciences. He is also an Adjunct professor at the Department of Computer Science and Operations Research at University of Montreal and a Core Academic member at Mila - Quebec AI Institute. He is a Canada CIFAR AI Chair and the Founder of BioGeometry, an AI startup that focuses on generative AI for antibody discovery. Tang’s main research interests are deep generative models and graph machine learning, and their applications to drug discovery. He is an international leader in graph machine learning, and LINE, his node representation method, has been widely recognized and cited more than five thousand times. He has also done pioneering work on AI for drug discovery, such as developing the first open-source machine learning frameworks for drug discovery, TorchDrug and TorchProtein.

Current Students

Huiyu Cai

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

Xixian Liu

PhD - Université de Montréal

Jiarui Lu

PhD - Université de Montréal

Chence Shi

PhD - Université de Montréal

Sophie Xhonneux

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

Xinyu Yuan

PhD - Université de Montréal

Zhihao Zhan

PhD - Université de Montréal

Zuobai Zhang

PhD - Université de Montréal

PhD - HEC Montréal

Jianan Zhao

PhD - Université de Montréal

Publications

Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials

Shengchao Liu

Weitao Du

Yanjing Li

Zhuoxinran Li

Zhiling Zheng

Chenru Duan

Zhiming Ma

Omar Yaghi

Anima Anandkumar

Christian Borgs

Jennifer Chayes

Hongyu Guo

Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific comm… (see more)unities, particularly in the domains of chemistry, biology, and material discovery. For these scientific problems, molecules serve as the fundamental building blocks, and machine learning has emerged as a highly effective and powerful tool for modeling their geometric structures. Nevertheless, due to the rapidly evolving process of the field and the knowledge gap between science (e.g., physics, chemistry, & biology) and machine learning communities, a benchmarking study on geometrical representation for such data has not been conducted. To address such an issue, in this paper, we first provide a unified view of the current symmetry-informed geometric methods, classifying them into three main categories: invariance, equivariance with spherical frame basis, and equivariance with vector frame basis. Then we propose a platform, coined Geom3D, which enables benchmarking the effectiveness of geometric strategies. Geom3D contains 16 advanced symmetry-informed geometric representation models and 14 geometric pretraining methods over 46 diverse datasets, including small molecules, proteins, and crystalline materials. We hope that Geom3D can, on the one hand, eliminate barriers for machine learning researchers interested in exploring scientific problems; and, on the other hand, provide valuable guidance for researchers in computational chemistry, structural biology, and materials science, aiding in the informed selection of representation techniques for specific applications.

2023-09-24

NeurIPS.cc/2023/Track/Datasets_and_Benchmarks (poster)

A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs

Ming Zhang

Maxime Gazeau

Reasoning on large-scale knowledge graphs has been long dominated by embedding methods. While path-based methods possess the inductive capac… (see more)ity that embeddings lack, their scalability is limited by the exponential number of paths. Here we present A*Net, a scalable path-based method for knowledge graph reasoning. Inspired by the A* algorithm for shortest path problems, our A*Net learns a priority function to select important nodes and edges at each iteration, to reduce time and memory footprint for both training and inference. The ratio of selected nodes and edges can be specified to trade off between performance and efficiency. Experiments on both transductive and inductive knowledge graph reasoning benchmarks show that A*Net achieves competitive performance with existing state-of-the-art path-based methods, while merely visiting 10% nodes and 10% edges at each iteration. On a million-scale dataset ogbl-wikikg2, A*Net not only achieves a new state-of-the-art result, but also converges faster than embedding methods. A*Net is the first path-based method for knowledge graph reasoning at such scale.

2023-09-20

NeurIPS.cc/2023/Conference (poster)

DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing

Yangtian Zhang

Zuobai Zhang

Bozitao Zhong

Sanchit Misra

Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. Ac… (see more)curately predicting the conformation of protein side-chains given their backbones is important for applications in protein structure prediction, design and protein-protein interactions. Traditional methods are computationally intensive and have limited accuracy, while existing machine learning methods treat the problem as a regression task and overlook the restrictions imposed by the constant covalent bond lengths and angles. In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space. To avoid issues arising from simultaneous perturbation of all four torsional angles, we propose autoregressively generating the four torsional angles from

2023-09-20

NeurIPS.cc/2023/Conference (poster)

GAUCHE: A Library for Gaussian Processes in Chemistry

Ryan-Rhys Griffiths

Leo Klarner

Henry Moss

Aditya Ravuri

Sang Truong

Bojana Rankovic

Samuel Stanton

Yuanqi Du

Arian Jamasb

Gary Tom

Julius Schwartz

Austin Tripp

Aryan Deshwal

Gregory Kell

Anthony Bourached

Alex J. Chan

Jacob Moss

Chengzhi Guo

Simon Frieder

Alpha A. Lee … (see 8 more)

Philippe Schwaller

Johannes Durholt

Saudamini Chaurasia

Ji Won Park

Felix Strieth-Kalthoff

Bingqing Cheng

Alán 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

2023-09-20

NeurIPS.cc/2023/Conference (poster)

Pretrainable geometric graph neural network for antibody affinity maturation

Huiyu Cai

Zuobai Zhang

Mingkai Wang

Bozitao Zhong

Yanling Wu

Tianlei Ying

Increasing the binding affinity of an antibody to its target antigen is a crucial task in antibody therapeutics development. This paper pres… (see more)ents a pretrainable geometric graph neural network, GearBind, and explores its potential inin silicoaffinity maturation. Leveraging multi-relational graph construction, multi-level geometric message passing and contrastive pretraining on mass-scale, unlabeled protein structural data, GearBind outperforms previous state-of-the-art approaches on SKEMPI and an independent test set. A powerful ensemble model based on GearBind is then derived and used to successfully enhance the binding of two antibodies with distinct formats and target antigens. ELISA EC50values of the designed antibody mutants are decreased by up to 17 fold, andKDvalues by up to 6.1 fold. These promising results underscore the utility of geometric deep learning and effective pretraining in macromolecule interaction modeling tasks.

2023-08-10

bioRxiv (preprint)

Scientific discovery in the age of artificial intelligence

Hanchen Wang

Tianfan Fu

Yuanqi Du

Wenhao Gao

Kexin Huang

Ziming Liu

Payal Chandak

Shengchao Liu

Peter Van Katwyk

Andreea Deac

Animashree Anandkumar

K. Bergen

Carla P. Gomes

Shirley Ho

Pushmeet Kohli

Joan Lasenby

Jure Leskovec

Tie-Yan Liu

A. Manrai

Debora Susan Marks … (see 10 more)

Bharath Ramsundar

Le Song

Jimeng Sun

Petar Veličković

MAX WELLING

Linfeng Zhang

Connor Wilson. Coley

Yoshua Bengio

Marinka Žitnik

2023-07-31

Nature (published)

A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining

Shengchao Liu

Weitao Du

Zhiming Ma

Hongyu Guo

Molecule pretraining has quickly become the go-to schema to boost the performance of AI-based drug discovery. Naturally, molecules can be re… (see more)presented as 2D topological graphs or 3D geometric point clouds. Although most existing pertaining methods focus on merely the single modality, recent research has shown that maximizing the mutual information (MI) between such two modalities enhances the molecule representation ability. Meanwhile, existing molecule multi-modal pretraining approaches approximate MI based on the representation space encoded from the topology and geometry, thus resulting in the loss of critical structural information of molecules. To address this issue, we propose MoleculeSDE. MoleculeSDE leverages group symmetric (e.g., SE(3)-equivariant and reflection-antisymmetric) stochastic differential equation models to generate the 3D geometries from 2D topologies, and vice versa, directly in the input space. It not only obtains tighter MI bound but also enables prosperous downstream tasks than the previous work. By comparing with 17 pretraining baselines, we empirically verify that MoleculeSDE can learn an expressive representation with state-of-the-art performance on 26 out of 32 downstream tasks.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (published)

proceedings.mlr.press

ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts

Minghao Xu

Xinyu Yuan

Santiago Miret

Current protein language models (PLMs) learn protein representations mainly based on their sequences, thereby well capturing co-evolutionary… (see more) information, but they are unable to explicitly acquire protein functions, which is the end goal of protein representation learning. Fortunately, for many proteins, their textual property descriptions are available, where their various functions are also described. Motivated by this fact, we first build the ProtDescribe dataset to augment protein sequences with text descriptions of their functions and other important properties. Based on this dataset, we propose the ProtST framework to enhance Protein Sequence pre-training and understanding by biomedical Texts. During pre-training, we design three types of tasks, i.e., unimodal mask prediction, multimodal representation alignment and multimodal mask prediction, to enhance a PLM with protein property information with different granularities and, at the same time, preserve the PLM's original representation power. On downstream tasks, ProtST enables both supervised learning and zero-shot prediction. We verify the superiority of ProtST-induced PLMs over previous ones on diverse representation learning benchmarks. Under the zero-shot setting, we show the effectiveness of ProtST on zero-shot protein classification, and ProtST also enables functional protein retrieval from a large-scale database without any function annotation.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (published)

proceedings.mlr.press

Signed Laplacian Graph Neural Networks

Yu Li

Meng Qu

Yi Chang

This paper studies learning meaningful node representations for signed graphs, where both positive and negative links exist. This problem ha… (see more)s been widely studied by meticulously designing expressive signed graph neural networks, as well as capturing the structural information of the signed graph through traditional structure decomposition methods, e.g., spectral graph theory. In this paper, we propose a novel signed graph representation learning framework, called Signed Laplacian Graph Neural Network (SLGNN), which combines the advantages of both. Specifically, based on spectral graph theory and graph signal processing, we first design different low-pass and high-pass graph convolution filters to extract low-frequency and high-frequency information on positive and negative links, respectively, and then combine them into a unified message passing framework. To effectively model signed graphs, we further propose a self-gating mechanism to estimate the impacts of low-frequency and high-frequency information during message passing. We mathematically establish the relationship between the aggregation process in SLGNN and signed Laplacian regularization in signed graphs, and theoretically analyze the expressiveness of SLGNN. Experimental results demonstrate that SLGNN outperforms various competitive baselines and achieves state-of-the-art performance.

2023-06-25

Proceedings of the AAAI Conference on Artificial Intelligence (published)

Score-based Enhanced Sampling for Protein Molecular Dynamics

Jiarui Lu

Bozitao Zhong

The dynamic nature of proteins is crucial for determining their biological functions and properties, and molecular dynamics (MD) simulations… (see more) stand as a predominant tool to study such phenomena. By utilizing empirically derived force fields, MD simulations explore the conformational space through numerically evolving the system along MD trajectories. However, the high-energy barrier of the force fields can hamper the exploration of MD, resulting in inadequately sampled ensemble. In this paper, we propose leveraging score-based generative models (SGMs) trained on large-scale general protein structures to perform protein con- formational sampling to complement traditional MD simulations. Experimental results demonstrate the effectiveness of our approach on several benchmark systems by comparing the results with long MD trajectories and state-of-the-art generative structure prediction models.

2023-06-18

ICML.cc/2023/Workshop/SPIGM (poster)

Evolving Computation Graphs

Andreea Deac

Graph neural networks (GNNs) have demonstrated success in modeling relational data, especially for data that exhibits homophily: when a conn… (see more)ection between nodes tends to imply that they belong to the same class. However, while this assumption is true in many relevant situations, there are important real-world scenarios that violate this assumption, and this has spurred research into improving GNNs for these cases. In this work, we propose Evolving Computation Graphs (ECGs), a novel method for enhancing GNNs on heterophilic datasets. Our approach builds on prior theoretical insights linking node degree, high homophily, and inter vs intra-class embedding similarity by rewiring the GNNs' computation graph towards adding edges that connect nodes that are likely to be in the same class. We utilise weaker classifiers to identify these edges, ultimately improving GNN performance on non-homophilic data as a result. We evaluate ECGs on a diverse set of recently-proposed heterophilous datasets and demonstrate improvements over the relevant baselines. ECG presents a simple, intuitive and elegant approach for improving GNN performance on heterophilic datasets without requiring prior domain knowledge.

2023-06-17

ICML.cc/2023/Workshop/TAGML (poster)

Biomedical discovery through the integrative biomedical knowledge hub (iBKH)

Chang Su

Yu Hou

Manqi Zhou

Suraj Rajendran

Jacqueline R.M. A. Maasch

Zehra Abedi

Haotan Zhang

Zilong Bai

Anthony Cuturrufo

Winston Guo

Fayzan F. Chaudhry

Gregory Ghahramani

Feixiong Cheng

Yue Li

Rui Zhang

Steven T. DeKosky

Jiang Bian

Fei Wang

Summary The massive and continuously increasing volume of biomedical knowledge derived from biological experiments or gained from healthcare… (see more) practices has become an invaluable treasure for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In the present study, we harmonized and integrated data from diverse biomedical resources to curate a comprehensive BKG, named the integrative Biomedical Knowledge Hub (iBKH). To facilitate the usage of iBKH in biomedical research, we developed a web-based, easy-to-use, publicly available graphical portal that allows fast, interactive, and visualized knowledge retrieval in iBKH. Furthermore, an efficient and scalable graph learning pipeline was developed for novel knowledge discovery in iBKH. As a proof of concept, we performed our iBKH-based method for computational in silico drug repurposing for Alzheimer’s disease. The iBKH is publicly available at: http://ibkh.ai/ .

2023-03-31

iScience (published)