Portrait de Jian Tang

Jian Tang

Membre académique principal
tangjian@mila.quebec
Chaire en IA Canada-CIFAR
Professeur agrégé, HEC Montréal, Département de sciences de la décision
Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle (DIRO)
Fondateur, BioGeometry
Sujets de recherche
Biologie computationnelle
Grands modèles de langage (LLM)
IA pour la science
Modèles génératifs
Modélisation moléculaire
Réseaux de neurones en graphes
Google Scholar

Biographie

Jian Tang est professeur agrégé au département de sciences de la décision de HEC. Il est aussi professeur associé au département informatique et recherche opérationnelle (DIRO) de l'Université de Montréal et un membre académique principal à Mila – Institut québécois d’intelligence artificielle. Il est titulaire d'une chaire de recherche en IA Canada-CIFAR et le fondateur de BioGeometry, une entreprise en démarrage spécialisée dans l'IA générative pour la découverte d'anticorps. Ses principaux domaines de recherche sont les modèles génératifs profonds, l'apprentissage automatique des graphes et leurs applications à la découverte de médicaments. Il est un leader international dans le domaine de l'apprentissage automatique des graphes, et son travail représentatif sur l'apprentissage de la représentation des nœuds, LINE, a été largement reconnu et cité plus de 5 000 fois. Il a également réalisé de nombreux travaux pionniers sur l'IA pour la découverte de médicaments, notamment le premier cadre d'apprentissage automatique à source ouverte pour la découverte de médicaments, TorchDrug et TorchProtein.

Étudiants actuels

Publications

ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
Minghao Xu
Xinyu Yuan
Santiago Miret
Jian Tang
Current protein language models (PLMs) learn protein representations mainly based on their sequences, thereby well capturing co-evolutionary… (voir plus) 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.
2023-07-24
International Conference on Machine Learning (Accept (Oral & Poster))
doi.org
proceedings.mlr.press
Flaky Performances When Pretraining on Relational Databases (Student Abstract)
Shengchao Liu
David Vázquez
Jian Tang
Pierre-Andre Noel
We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs extra… (voir plus)cted from relational databases (RDBs). Intuitively, this joint use of SSL and GNNs should allow to leverage more of the available data, which could translate to better results. However, we found that naively porting contrastive SSL techniques can cause ``negative transfer'': linear evaluation on fixed representation from a pretrained model performs worse than on representations from the randomly-initialized model. Based on the conjecture that contrastive SSL conflicts with the message passing layers of the GNN, we propose InfoNode: a contrastive loss aiming to maximize the mutual information between a node's initial- and final-layer representation. The primary empirical results support our conjecture and the effectiveness of InfoNode.
2023-06-25
AAAI Conference on Artificial Intelligence (publié)
doi.org
Signed Laplacian Graph Neural Networks
Yu Li
Meng Qu
Jian Tang
Yi Chang
This paper studies learning meaningful node representations for signed graphs, where both positive and negative links exist. This problem ha… (voir plus)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 (publié)
doi.org
Score-based Enhanced Sampling for Protein Molecular Dynamics
Jiarui Lu
Bozitao Zhong
Jian Tang
The dynamic nature of proteins is crucial for determining their biological functions and properties, and molecular dynamics (MD) simulations… (voir plus) 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)
openreview.net
openreview.net
Evolving Computation Graphs
Andreea Deac
Jian Tang
Graph neural networks (GNNs) have demonstrated success in modeling relational data, especially for data that exhibits homophily: when a conn… (voir plus)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)
doi.org
openreview.net
E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
Yangtian Zhang
Huiyu Cai
Chence Shi
Bozitao Zhong
Jian Tang
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… (voir plus)uses on blind flexible selfdocking, 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.
2023-04-30
International Conference on Learning Representations (Accept: poster)
doi.org
openreview.net
Learning on Large-Scale Text-Attributed Graphs via Variational Inference
Jianan Zhao
Meng Qu
Chaozhuo Li
Hao Yan
Qian Liu
Rui Li
Xing Xie
Jian Tang
This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for s… (voir plus)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.
2023-04-30
International Conference on Learning Representations (Accept: notable-top-5%)
doi.org
openreview.net
Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching
Shengchao Liu
Hongyu Guo
Jian Tang
Molecular representation pretraining is critical in various applications for drug and material discovery due to the limited number of labele… (voir plus)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.
2023-04-30
International Conference on Learning Representations (Accept: poster)
doi.org
openreview.net
Protein Representation Learning by Geometric Structure Pretraining
Zuobai Zhang
Minghao Xu
Arian 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… (voir plus)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.
2023-04-30
International Conference on Learning Representations (Accept: poster)
doi.org
openreview.net
Protein Sequence and Structure Co-design with Equivariant Translation
Chence Shi
Chuanrui Wang
Jiarui Lu
Bozitao Zhong
Jian Tang
Proteins are macromolecules that perform essential functions in all living organisms. Designing novel proteins with specific structures and … (voir plus)desired functions has been a long-standing challenge in the field of bioengineering. Existing approaches generate both protein sequence and structure using either autoregressive models or diffusion models, both of which suffer from high inference costs. In this paper, we propose a new approach capable of protein sequence and structure co-design, which iteratively translates both protein sequence and structure into the desired state from random initialization, based on context features given a priori. Our model consists of a trigonometry-aware encoder that reasons geometrical constraints and interactions from context features, and a roto-translation equivariant decoder that translates protein sequence and structure interdependently. Notably, all protein amino acids are updated in one shot in each translation step, which significantly accelerates the inference process. Experimental results across multiple tasks show that our model outperforms previous state-of-the-art baselines by a large margin, and is able to design proteins of high fidelity as regards both sequence and structure, with running time orders of magnitude less than sampling-based methods.
2023-04-30
International Conference on Learning Representations (Accept: poster)
doi.org
openreview.net
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
Jian Tang
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… (voir plus) 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 (publié)
doi.org
A Systematic Study of Joint Representation Learning on Protein Sequences and Structures
Zuobai Zhang
Chuanrui Wang
Minghao Xu
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 functions. Recent sequenc… (voir plus)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.
2023-03-10
ArXiv (prépublication)
arxiv.org
arxiv.org