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Shengchao Liu

Alumni

Publications

Flaky Performances when Pretraining on Relational Databases
Shengchao Liu
David Vázquez
Jian Tang
Pierre-Andre Noel
2022-11-08
ArXiv (prépublication)
doi.org
arxiv.org
Flaky Performances when Pre-Training on Relational Databases with a Plan for Future Characterization Efforts
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). 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.
2022-07-21
ICML.cc/2022/Workshop/Pre-Training (inconnu)
openreview.net
openreview.net
Structured Multi-task Learning for Molecular Property Prediction
Shengchao Liu
Meng Qu
Zuobai Zhang
Huiyu Cai
Jian Tang
Multi-task learning for molecular property prediction is becoming increasingly important in drug discovery. However, in contrast to other do… (voir plus)mains, the performance of multi-task learning in drug discovery is still not satisfying as the number of labeled data for each task is too limited, which calls for additional data to complement the data scarcity. In this paper, we study multi-task learning for molecular property prediction in a novel setting, where a relation graph between tasks is available. We first construct a dataset (ChEMBL-STRING) including around 400 tasks as well as a task relation graph. Then to better utilize such relation graph, we propose a method called SGNN-EBM to systematically investigate the structured task modeling from two perspectives. (1) In the \emph{latent} space, we model the task representations by applying a state graph neural network (SGNN) on the relation graph. (2) In the \emph{output} space, we employ structured prediction with the energy-based model (EBM), which can be efficiently trained through noise-contrastive estimation (NCE) approach. Empirical results justify the effectiveness of SGNN-EBM. Code is available on https://github.com/chao1224/SGNN-EBM.
2022-03-27
International Conference on Artificial Intelligence and Statistics (Accept (Poster))
doi.org
proceedings.mlr.press
Pre-training Molecular Graph Representation with 3D Geometry
Shengchao Liu
Hanchen Wang
Weiyang Liu
Joan Lasenby
Hongyu Guo
Jian Tang
Molecular graph representation learning is a fundamental problem in modern drug and material discovery. Molecular graphs are typically model… (voir plus)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. Code is available on GitHub: https://github.com/chao1224/GraphMVP.
2022-01-27
ICLR.cc/2022/Conference (poster)
openreview.net
openreview.net
Learning to Navigate the Synthetically Accessible Chemical Space Using Reinforcement Learning
Sai Krishna Gottipati
Boris Sattarov
Sufeng Niu
Yashaswi Pathak
Haoran Wei
Shengchao Liu
Karam J. Thomas
Simon Blackburn
Connor W. Coley
Jian Tang
Sarath Chandar
Yoshua Bengio
Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep gen… (voir plus)erative models. However, current generative approaches exhibit a significant challenge as they do not ensure that the proposed molecular structures can be feasibly synthesized nor do they provide the synthesis routes of the proposed small molecules, thereby seriously limiting their practical applicability. In this work, we propose a novel forward synthesis framework powered by reinforcement learning (RL) for de novo drug design, Policy Gradient for Forward Synthesis (PGFS), that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo drug design system. In this setup, the agent learns to navigate through the immense synthetically accessible chemical space by subjecting commercially available small molecule building blocks to valid chemical reactions at every time step of the iterative virtual multi-step synthesis process. The proposed environment for drug discovery provides a highly challenging test-bed for RL algorithms owing to the large state space and high-dimensional continuous action space with hierarchical actions. PGFS achieves state-of-the-art performance in generating structures with high QED and penalized clogP. Moreover, we validate PGFS in an in-silico proof-of-concept associated with three HIV targets. Finally, we describe how the end-to-end training conceptualized in this study represents an important paradigm in radically expanding the synthesizable chemical space and automating the drug discovery process.
2020-07-13
International Conference on Machine Learning (Accept)
doi.org
proceedings.mlr.press
S UPPLEMENTARY M ATERIAL - L EARNING T O N AVIGATE T HE S YNTHETICALLY A CCESSIBLE C HEMICAL S PACE U SING R EINFORCEMENT L EARNING
Sai Krishna
Gottipati
B. Sattarov
Sufeng Niu
Yashaswi Pathak
Haoran Wei
Shengchao Liu
Karam M. J. Thomas
Simon R. Blackburn
Connor Wilson. Coley
Jian Tang
A. Chandar
Yoshua Bengio
While updating the critic network, we multiply the normal random noise vector with policy noise of 0.2 and then clip it in the range -0.2 to… (voir plus) 0.2. This clipped policy noise is added to the action at the next time step a′ computed by the target actor networks f and π. The actor networks (f and π networks), target critic and target actor networks are updated once every two updates to the critic network.
2019-12-31
(publié)
www.semanticscholar.org