59 citations · 343 across the 19 of their papers we have counts for
19 papers
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Shengchao Liu, Weitao Du, Yanjing Li +10
Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of…
Machine-Learning-Assisted and Real-Time-Feedback-Controlled Growth of InAs/GaAs Quantum Dots
Chao Shen, Wenkang Zhan, Kaiyao Xin +11
Self-assembled InAs/GaAs quantum dots (QDs) have properties highly valuable for developing various optoelectronic devices such as QD lasers and single photon sources. The applicati…
Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling
Jiarui Lu, Bozitao Zhong, Zuobai Zhang +1
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 pre…
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
Yangtian Zhang, Zuobai Zhang, Bozitao Zhong +2
Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. Accurately predicting the conformation of…
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
Shengchao Liu, Weitao Du, Zhiming Ma +2
Molecule pretraining has quickly become the go-to schema to boost the performance of AI-based drug discovery. Naturally, molecules can be represented as 2D topological graphs or 3D…
A Systematic Study of Joint Representation Learning on Protein Sequences and Structures
Zuobai Zhang, Chuanrui Wang, Minghao Xu +4
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequence representation learning methods based…
A Text-guided Protein Design Framework
Shengchao Liu, Yanjing Li, Zhuoxinran Li +10
Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in the text format…
Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction
Zuobai Zhang, Minghao Xu, Aurélie Lozano +3
Self-supervised pre-training methods on proteins have recently gained attention, with most approaches focusing on either protein sequences or structures, neglecting the exploration…
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
Minghao Xu, Xinyu Yuan, Santiago Miret +1
Current protein language models (PLMs) learn protein representations mainly based on their sequences, thereby well capturing co-evolutionary information, but they are unable to exp…
Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
Shengchao Liu, Weili Nie, Chengpeng Wang +6
There is increasing adoption of artificial intelligence in drug discovery. However, existing studies use machine learning to mainly utilize the chemical structures of molecules but…
Protein Sequence and Structure Co-Design with Equivariant Translation
Chence Shi, Chuanrui Wang, Jiarui Lu +2
Proteins are macromolecules that perform essential functions in all living organisms. Designing novel proteins with specific structures and desired functions has been a long-standi…
E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
Yangtian Zhang, Huiyu Cai, Chence Shi +2
In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery. This work focuses on blind flexible selfdocking, wher…
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Songtao Liu, Zhengkai Tu, Minkai Xu +6
Retrosynthetic planning aims to devise a complete multi-step synthetic route from starting materials to a target molecule. Current strategies use a decoupled approach of single-ste…
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
Minghao Xu, Zuobai Zhang, Jiarui Lu +5
We are now witnessing significant progress of deep learning methods in a variety of tasks (or datasets) of proteins. However, there is a lack of a standard benchmark to evaluate th…
Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction
Kuangqi Zhou, Kaixin Wang, Jiashi Feng +3
How to accurately predict the properties of molecules is an essential problem in AI-driven drug discovery, which generally requires a large amount of annotation for training deep l…
Neural-Symbolic Models for Logical Queries on Knowledge Graphs
Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang +1
Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning. Traditional symbolic methods traverse a complete knowledge grap…
Protein Representation Learning by Geometric Structure Pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb +4
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain prote…
Structured Multi-task Learning for Molecular Property Prediction
Shengchao Liu, Meng Qu, Zuobai Zhang +2
Multi-task learning for molecular property prediction is becoming increasingly important in drug discovery. However, in contrast to other domains, the performance of multi-task lea…
Learning Neural Generative Dynamics for Molecular Conformation Generation
Minkai Xu, Shitong Luo, Yoshua Bengio +2
We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph. Traditional methods, such as molecular dynamics, sample conformations via computationa…