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

huiyu.cai@mila.quebec

Zewen Chi

Research Intern - Beijing Institute of Technology

Andreea-Ioana Deac

PhD - Université de Montréal

Research Intern - HEC Montréal

Michael Galkin

Collaborating researcher

Farzaneh Heidari

PhD - Université de Montréal

Principal supervisor :

Guillaume Rabusseau

Stephen Lu

Research Intern - McGill University

stephen.lu@mila.quebec

Jerry Lu

PhD - Université de Montréal

Meng Qu

PhD - Université de Montréal

Chence Shi

PhD - Université de Montréal

Walter Virany

Collaborating researcher

Chuanru Wang

Master's Research - Université de Montréal

Sophie Xhonneux

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

lpxhonneux@gmail.com

Minghao Xu

Collaborating researcher

Xinyu Yuan

PhD - Université de Montréal

Zhihao Zhan

PhD - Université de Montréal

Zuobai Zhang

PhD - Université de Montréal

zhaocheng.zhu@umontreal.ca

Jianan Zhao

PhD - Université de Montréal

PhD - Université de Montréal

Publications

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.

2023-04-24

ICML.cc/2023/Conference (published)

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

2023-04-01

iScience (published)

A Systematic Study of Joint Representation Learning on Protein Sequences and Structures

Zuobai Zhang

Chuanrui Wang

Minghao Xu

Vijil Chenthamarakshan

Aurelie Lozano

Payel Das

Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequenc… (see more)e representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a challenge. In contrast, structure-based methods leverage 3D structural information with graph neural networks and geometric pre-training methods show potential in function prediction tasks, but still suffers from the limited number of available structures. To bridge this gap, our study undertakes a comprehensive exploration of joint protein representation learning by integrating a state-of-the-art PLM (ESM-2) with distinct structure encoders (GVP, GearNet, CDConv). We introduce three representation fusion strategies and explore different pre-training techniques. Our method achieves significant improvements over existing sequence- and structure-based methods, setting new state-of-the-art for function annotation. This study underscores several important design choices for fusing protein sequence and structure information. Our implementation is available at https://github.com/DeepGraphLearning/ESM-GearNet.

2023-03-11

ArXiv (preprint)

arxiv.org

Enhancing Protein Language Model with Structure-based Encoder and Pre-training

Zuobai Zhang

Minghao Xu

Aurelie Lozano

Vijil Chenthamarakshan

Payel Das

Protein language models (PLMs) pre-trained on large-scale protein sequence corpora have achieved impressive performance on various downstrea… (see more)m protein understanding tasks. Despite the ability to implicitly capture inter-residue contact information, transformer-based PLMs cannot encode protein structures explicitly for better structure-aware protein representations. Besides, the power of pre-training on available protein structures has not been explored for improving these PLMs, though structures are important to determine functions. To tackle these limitations, in this work, we enhance the PLM with structure-based encoder and pre-training. We first explore feasible model architectures to combine the advantages of a state-of-the-art PLM (i.e., ESM-1b) and a state-of-the-art protein structure encoder (i.e., GearNet). We empirically verify the ESM-GearNet that connects two encoders in a series way as the most effective combination model. To further improve the effectiveness of ESM-GearNet, we pre-train it on massive unlabeled protein structures with contrastive learning, which aligns representations of co-occurring subsequences so as to capture their biological correlation. Extensive experiments on EC and GO protein function prediction benchmarks demonstrate the superiority of ESM-GearNet over previous PLMs and structure encoders, and clear performance gains are further achieved by structure-based pre-training upon ESM-GearNet. The source code will be made public upon acceptance.

2023-03-06

ICLR.cc/2023/Workshop/MLDD (poster)

Enhancing Protein Language Model with Structure-based Encoder and Pre-training

Zuobai Zhang

Minghao Xu

Aurelie Lozano

Vijil Chenthamarakshan

Payel Das

2023-03-06

ICLR.cc/2023/Workshop/MLDD (poster)

EurNet: Efficient Multi-Range Relational Modeling of Protein Structure

Minghao Xu

Yuanfan Guo

Yi Xu

Xinlei Chen

Yuandong Tian

Modeling the 3D structures of proteins is critical for obtaining effective protein structure representations, which further boosts protein f… (see more)unction understanding. Existing protein structure encoders mainly focus on modeling short-range interactions within protein structures, while they neglect modeling the interactions at multiple length scales that are actually complete interactive patterns in protein structures. To attain complete interaction modeling with efficient computation, we introduce the EurNet for Efficient multi-range relational modeling. In EurNet, we represent the protein structure as a multi-relational residue-level graph with different types of edges for modeling short-range, medium-range and long-range interactions. To efficiently process these different interactive relations, we propose a novel modeling layer, called Gated Relational Message Passing (GRMP), as the basic building block of EurNet. GRMP can capture multiple interactive relations in protein structures with little extra computational cost. We verify the state-of-the-art performance of EurNet on EC and GO protein function prediction benchmarks, and the proposed GRMP layer is proved to achieve better efficiency-performance trade-off than the widely-used relational graph convolution.

2023-03-06

ICLR.cc/2023/Workshop/MLDD (poster)

A Text-guided Protein Design Framework

Shengchao Liu

Yutao Zhu

Jiarui Lu

Zhao Xu

Weili Nie

Anthony James Gitter

Chaowei Xiao

Hongyu Guo

Animashree Anandkumar

2023-02-09

ArXiv (preprint)

arxiv.org

E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking

Yang Zhang

Huiyu Cai

Chence Shi

Bozitao Zhong

In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery. This work foc… (see more)uses on blind flexible self-docking, where we aim to predict the positions, orientations and conformations of docked molecules. Traditional physics-based methods usually suffer from inaccurate scoring functions and high inference cost. Recently, data-driven methods based on deep learning techniques are attracting growing interest thanks to their efficiency during inference and promising performance. These methods usually either adopt a two-stage approach by first predicting the distances between proteins and ligands and then generating the final coordinates based on the predicted distances, or directly predicting the global roto-translation of ligands. In this paper, we take a different route. Inspired by the resounding success of AlphaFold2 for protein structure prediction, we propose E3Bind, an end-to-end equivariant network that iteratively updates the ligand pose. E3Bind models the protein-ligand interaction through careful consideration of the geometric constraints in docking and the local context of the binding site. Experiments on standard benchmark datasets demonstrate the superior performance of our end-to-end trainable model compared to traditional and recently-proposed deep learning methods.

2023-02-01

ICLR.cc/2023/Conference (poster)

EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data

Minghao Xu

Yuanfan Guo

Yi Xu

Xinlei Chen

Yuandong Tian

Modeling spatial relationship in the data remains critical across many different tasks, such as image classification, semantic segmentation … (see more)and protein structure understanding. Previous works often use a unified solution like relative positional encoding. However, there exists different kinds of spatial relations, including short-range, medium-range and long-range relations, and modeling them separately can better capture the focus of different tasks on the multi-range relations (e.g., short-range relations can be important in instance segmentation, while long-range relations should be upweighted for semantic segmentation). In this work, we introduce the EurNet for Efficient multi-range relational modeling. EurNet constructs the multi-relational graph, where each type of edge corresponds to short-, medium- or long-range spatial interactions. In the constructed graph, EurNet adopts a novel modeling layer, called gated relational message passing (GRMP), to propagate multi-relational information across the data. GRMP captures multiple relations within the data with little extra computational cost. We study EurNets in two important domains for image and protein structure modeling. Extensive experiments on ImageNet classification, COCO object detection and ADE20K semantic segmentation verify the gains of EurNet over the previous SoTA FocalNet. On the EC and GO protein function prediction benchmarks, EurNet consistently surpasses the previous SoTA GearNet. Our results demonstrate the strength of EurNets on modeling spatial multi-relational data from various domains.

2023-02-01

ICLR.cc/2023/Conference (rejected)

GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow

Fang Sun

Zhihao Zhan

Hongyu Guo

Ming Zhang

Designing molecules that bind to specific target proteins is a fundamental task in drug discovery. Recent generative models leveraging geome… (see more)trical constraints imposed by proteins and molecules have shown great potential in generating protein-specific 3D molecules. Nevertheless, these existing methods fail to generate 3D molecules with 2D skeletal curtailments, which encode pharmacophoric patterns essential to drug potency. To cope with this challenge, we propose GraphVF, which seamlessly integrates geometrical and skeletal restraints into a variational flow framework, where the former is captured through a flow transformation and the latter is encoded by an amortized factorized Gaussian. We empirically verify that our method achieves state-of-the-art performance on protein-specific 3D molecule generation in terms of binding affinity and some other drug properties. In particular, it represents the first controllable geometry-aware, protein-specific molecule generation method, which enables creating 3D molecules with specified chemical sub-structures or drug properties.

2023-02-01

ICLR.cc/2023/Conference (rejected)

Learning on Large-scale Text-attributed Graphs via Variational Inference

Jianan Zhao

Meng Qu

Chaozhuo Li

Hao Yan

Qian Liu

Rui Li

Xing Xie

This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for s… (see more)uch a problem would be integrating both the text and graph structure information with large language models and graph neural networks (GNNs). However, the problem becomes very challenging when graphs are large due to the high computational complexity brought by training large language models and GNNs together. In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM. Instead of simultaneously training large language models and GNNs on big graphs, GLEM proposes to alternatively update the two modules in the E-step and M-step. Such a procedure allows training the two modules separately while simultaneously allowing the two modules to interact and mutually enhance each other. Extensive experiments on multiple data sets demonstrate the efficiency and effectiveness of the proposed approach.

2023-02-01

ICLR.cc/2023/Conference (notable)

Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching

Shengchao Liu

Hongyu Guo

Molecular representation pretraining is critical in various applications for drug and material discovery due to the limited number of labele… (see more)d molecules, and most existing work focuses on pretraining on 2D molecular graphs. However, the power of pretraining on 3D geometric structures has been less explored. This is owing to the difficulty of finding a sufficient proxy task that can empower the pretraining to effectively extract essential features from the geometric structures. Motivated by the dynamic nature of 3D molecules, where the continuous motion of a molecule in the 3D Euclidean space forms a smooth potential energy surface, we propose GeoSSL, a 3D coordinate denoising pretraining framework to model such an energy landscape. Further by leveraging an SE(3)-invariant score matching method, we propose GeoSSL-DDM in which the coordinate denoising proxy task is effectively boiled down to denoising the pairwise atomic distances in a molecule. Our comprehensive experiments confirm the effectiveness and robustness of our proposed method.

2023-02-01

ICLR.cc/2023/Conference (poster)