Search Results for author: Zuobai Zhang

Found 16 papers, 12 papers with code

Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations

no code implementations16 Feb 2024 Jiarui Lu, Zuobai Zhang, Bozitao Zhong, Chence Shi, Jian Tang

The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consuming molecular dynamics (MD) simulations in silico.

Physical Simulations

Structure-Informed Protein Language Model

1 code implementation7 Feb 2024 Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie Lozano, Payel Das, Jian Tang

To address this issue, we introduce the integration of remote homology detection to distill structural information into protein language models without requiring explicit protein structures as input.

Protein Function Prediction Protein Language Model

Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling

1 code implementation5 Jun 2023 Jiarui Lu, Bozitao Zhong, Zuobai Zhang, Jian Tang

The dynamic nature of proteins is crucial for determining their biological functions and properties, for which Monte Carlo (MC) and molecular dynamics (MD) simulations stand as predominant tools to study such phenomena.

Benchmarking Denoising +1

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

1 code implementation NeurIPS 2023 Yangtian Zhang, Zuobai Zhang, Bozitao Zhong, Sanchit Misra, Jian Tang

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.

Denoising Protein Structure Prediction

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

3 code implementations11 Mar 2023 Zuobai Zhang, Chuanrui Wang, Minghao Xu, Vijil Chenthamarakshan, Aurélie Lozano, Payel Das, Jian Tang

Recent sequence 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.

Contrastive Learning Protein Function Prediction +1

FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning

1 code implementation30 Sep 2022 Songtao Liu, Zhengkai Tu, Minkai Xu, Zuobai Zhang, Lu Lin, Rex Ying, Jian Tang, Peilin Zhao, Dinghao Wu

Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route.

Drug Discovery In-Context Learning +3

PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding

1 code implementation5 Jun 2022 Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Chang Ma, Runcheng Liu, Jian Tang

However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field.

Feature Engineering Multi-Task Learning +2

Protein Representation Learning by Geometric Structure Pretraining

2 code implementations11 Mar 2022 Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, Jian Tang

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.

Contrastive Learning Property Prediction +1

Structured Multi-task Learning for Molecular Property Prediction

1 code implementation22 Feb 2022 Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang

However, in contrast to other domains, 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.

Drug Discovery Molecular Property Prediction +4

TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery

1 code implementation16 Feb 2022 Zhaocheng Zhu, Chence Shi, Zuobai Zhang, Shengchao Liu, Minghao Xu, Xinyu Yuan, Yangtian Zhang, Junkun Chen, Huiyu Cai, Jiarui Lu, Chang Ma, Runcheng Liu, Louis-Pascal Xhonneux, Meng Qu, Jian Tang

However, lacking domain knowledge (e. g., which tasks to work on), standard benchmarks and data preprocessing pipelines are the main obstacles for machine learning researchers to work in this domain.

BIG-bench Machine Learning Drug Discovery +2

Multi-task Learning with Domain Knowledge for Molecular Property Prediction

no code implementations NeurIPS Workshop AI4Scien 2021 Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang

In this paper, we study multi-task learning for molecule property prediction in a different setting, where a relation graph between different tasks is available.

Drug Discovery Molecular Property Prediction +4

Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction

1 code implementation NeurIPS 2021 Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, Jian Tang

To further improve the capacity of the path formulation, we propose the Neural Bellman-Ford Network (NBFNet), a general graph neural network framework that solves the path formulation with learned operators in the generalized Bellman-Ford algorithm.

Inductive Relation Prediction Link Prediction +1

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