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

Collaborating researcher

Farzana Heidari

PhD - Université de Montréal

Principal supervisor :

Xixian Liu

PhD - Université de Montréal

Jiarui Lu

PhD - Université de Montréal

Chence Shi

PhD - Université de Montréal

Yu Tianshu

Independent visiting researcher - Chinese University of Hong Kong

Sophie Xhonneux

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

lpxhonneux@gmail.com

Xinyu Yuan

PhD - Université de Montréal

xinyu.yuan@mila.quebec

Zhihao Zhan

PhD - Université de Montréal

PhD - HEC Montréal

PhD - Université de Montréal

Jianan Zhao

PhD - Université de Montréal

Publications

Physics-Inspired Protein Encoder Pre-Training via Siamese Sequence-Structure Diffusion Trajectory Prediction

Zuobai Zhang

Minghao Xu

Aurelie Lozano

Vijil Chenthamarakshan

Payel Das

Pre-training methods on proteins are recently gaining interest, leveraging either protein sequences or structures, while modeling their join… (see more)t energy landscape is largely unexplored. In this work, inspired by the success of denoising diffusion models, we propose the DiffPreT approach to pre-train a protein encoder by sequence-structure multimodal diffusion modeling. DiffPreT guides the encoder to recover the native protein sequences and structures from the perturbed ones along the multimodal diffusion trajectory, which acquires the joint distribution of sequences and structures. Considering the essential protein conformational variations, we enhance DiffPreT by a physics-inspired method called Siamese Diffusion Trajectory Prediction ( SiamDiff ) to capture the correlation between different conformers of a protein. SiamDiff attains this goal by maximizing the mutual information between representations of diffusion trajectories of structurally-correlated conformers. We study the effectiveness of DiffPreT and SiamDiff on both atom-and residue-level structure-based protein understanding tasks. Experimental results show that the performance of DiffPreT is consistently competitive on all tasks, and SiamDiff achieves new state-of-the-art performance, considering the mean ranks on all tasks. The source code will be released upon acceptance.

2023-01-01

arXiv.org (preprint)

GraphCG: Unsupervised Discovery of Steerable Factors in Graphs

Shengchao Liu

Chengpeng Wang

Weili Nie

Hanchen Wang

Jiarui Lu

Bolei Zhou

Deep generative models have been extensively explored recently, especially for the graph data such as molecular graphs and point clouds. Yet… (see more), much less investigation has been carried out on understanding the learned latent space of deep graph generative models. Such understandings can open up a unified perspective and provide guidelines for essential tasks like controllable generation. In this paper, we first examine the representation space of the recent deep generative model trained for graph data, observing that the learned representation space is not perfectly disentangled. Based on this observation, we then propose an unsupervised method called GraphCG, which is model-agnostic and task-agnostic for discovering steerable factors in graph data. Specifically, GraphCG learns the semantic-rich directions via maximizing the corresponding mutual information, where the edited graph along the same direction will possess certain steerable factors. We conduct experiments on two types of graph data, molecular graphs and point clouds. Both the quantitative and qualitative results show the effectiveness of GraphCG for discovering steerable factors. The code will be public in the near future.

2022-11-22

NeurIPS.cc/2022/Workshop/GLFrontiers (published)

Flaky Performances when Pretraining on Relational Databases

Shengchao Liu

David Vazquez

Pierre-Andre Noel

2022-11-09

ArXiv (preprint)

Deep generative molecular design reshapes drug discovery

Xiangxiang Zeng

Yi Wang

Yuan Luo

Seung-gu Kang

Felice C. Lightstone

Evandro F. Fang

Wendy Cornell

Ruth Nussinov

Feixiong Cheng

2022-10-01

Cell Reports Medicine (published)

Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs

Stephen Bonner

Ufuk Kirik

Ola Engkvist

Ian P Barrett

Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KGs) that utiliz… (see more)e the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modelling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modelling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition.

2022-07-26

Briefings in Bioinformatics (published)

Flaky Performances when Pre-Training on Relational Databases with a Plan for Future Characterization Efforts

Shengchao Liu

David Vazquez

Pierre-Andre Noel

We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs extra… (see more)cted from relational databases (RDBs). Intu-itively, this joint use of SSL and GNNs allows us to leverage more of the available data, which could translate to better results. However, while we observe positive transfer in some cases, others showed systematic performance degradation, including some spectacular ones. We hypothesize a mechanism that could explain this behaviour and draft the plan for future work testing it by characterizing how much relevant information different strategies can (theoretically and/or empirically) extract from (synthetic and/or real) RDBs.

2022-07-22

ICML.cc/2022/Workshop/Pre-Training (accepted)

Neural-Symbolic Models for Logical Queries on Knowledge Graphs

Zhaocheng Zhu

Mikhail Galkin

Zuobai Zhang

Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning. Traditional symbolic me… (see more)thods traverse a complete knowledge graph to extract the answers, which provides good interpretation for each step. Recent neural methods learn geometric embeddings for complex queries. These methods can generalize to incomplete knowledge graphs, but their reasoning process is hard to interpret. In this paper, we propose Graph Neural Network Query Executor (GNN-QE), a neural-symbolic model that enjoys the advantages of both worlds. GNN-QE decomposes a complex FOL query into relation projections and logical operations over fuzzy sets, which provides interpretability for intermediate variables. To reason about the missing links, GNN-QE adapts a graph neural network from knowledge graph completion to execute the relation projections, and models the logical operations with product fuzzy logic. Experiments on 3 datasets show that GNN-QE significantly improves over previous state-of-the-art models in answering FOL queries. Meanwhile, GNN-QE can predict the number of answers without explicit supervision, and provide visualizations for intermediate variables.

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (published)

Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction

Kuangqi Zhou

Kaixin Wang

Jiashi Feng

Tingyang Xu

Xinchao Wang

How to accurately predict the properties of molecules is an essential problem in AI-driven drug discovery, which generally requires a large … (see more)amount of annotation for training deep learning models. Annotating molecules, however, is quite costly because it requires lab experiments conducted by experts. To reduce annotation cost, deep Active Learning (AL) methods are developed to select only the most representative and informative data for annotating. However, existing best deep AL methods are mostly developed for a single type of learning task (e.g., single-label classification), and hence may not perform well in molecular property prediction that involves various task types. In this paper, we propose a Task-type-generic active learning framework (termed Tyger) that is able to handle different types of learning tasks in a unified manner. The key is to learn a chemically-meaningful embedding space and perform active selection fully based on the embeddings, instead of relying on task-type-specific heuristics (e.g., class-wise prediction probability) as done in existing works. Specifically, for learning the embedding space, we instantiate a querying module that learns to translate molecule graphs into corresponding SMILES strings. Furthermore, to ensure that samples selected from the space are both representative and informative, we propose to shape the embedding space by two learning objectives, one based on domain knowledge and the other leveraging feedback from the task learner (i.e., model that performs the learning task at hand). We conduct extensive experiments on benchmark datasets of different task types. Experimental results show that Tyger consistently achieves high AL performance on molecular property prediction, outperforming baselines by a large margin. We also perform ablative experiments to verify the effectiveness of each component in Tyger.

2022-05-23

ArXiv (preprint)

Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering

Jie Zhang

Xiaokang Zhang

Jifan Yu

Jie Tang

Cuiping Li

Hong Chen

2022-05-01

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)

Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering

Jing Zhang

Xiaokang Zhang

Jifan Yu

Jie Tang

Cuiping Li

Hong Chen

Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. The desired subgraph is crucial as a small… (see more) one may exclude the answer but a large one might introduce more noises. However, the existing retrieval is either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs, which increases the reasoning bias when the intermediate supervision is missing. This paper proposes a trainable subgraph retriever (SR) decoupled from the subsequent reasoning process, which enables a plug-and-play framework to enhance any subgraph-oriented KBQA model. Extensive experiments demonstrate SR achieves significantly better retrieval and QA performance than existing retrieval methods. Via weakly supervised pre-training as well as the end-to-end fine-tuning, SR achieves new state-of-the-art performance when combined with NSM (He et al., 2021), a subgraph-oriented reasoner, for embedding-based KBQA methods. Codes and datasets are available online (https://github.com/RUCKBReasoning/SubgraphRetrievalKBQA)

2022-02-27

ArXiv (preprint)

Pre-training Molecular Graph Representation with 3D Geometry

Shengchao Liu

Hanchen Wang

Weiyang Liu

Joan Lasenby

Hongyu Guo

Molecular graph representation learning is a fundamental problem in modern drug and material discovery. Molecular graphs are typically model… (see more)ed by their 2D topological structures, but it has been recently discovered that 3D geometric information plays a more vital role in predicting molecular functionalities. However, the lack of 3D information in real-world scenarios has significantly impeded the learning of geometric graph representation. To cope with this challenge, we propose the Graph Multi-View Pre-training (GraphMVP) framework where self-supervised learning (SSL) is performed by leveraging the correspondence and consistency between 2D topological structures and 3D geometric views. GraphMVP effectively learns a 2D molecular graph encoder that is enhanced by richer and more discriminative 3D geometry. We further provide theoretical insights to justify the effectiveness of GraphMVP. Finally, comprehensive experiments show that GraphMVP can consistently outperform existing graph SSL methods.

2022-01-28

ICLR.cc/2022/Conference (poster)

High-Order Pooling for Graph Neural Networks with Tensor Decomposition

Chenqing Hua

Guillaume Rabusseau