59 citations · 343 across the 19 of their papers we have counts for
10 papers · 1 filter
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…
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 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…
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…
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…
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…
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…