8 citations · 39 across the 26 of their papers we have counts for
17 papers · 1 filter
Generative Retrieval for Reverse Target Fishing with a Proteome-Scale Benchmark
Zhichao Deng, Haichuan Tan, Xin Hong +4
Reverse target fishing identifies protein targets for a query molecule and is central to drug discovery and repurposing. Discriminative approaches adapted from virtual screening fa…
MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization
Xiangsen Chen, Ruilong Wu, Yanyan Lan +2
Despite deep learning's success in chemistry, its impact is hindered by a lack of interpretability and an inability to resolve activity cliffs, where minor structural nuances trigg…
SDrug: Bridging Protein Sequence and 3D Structure in Contrastive Representation Learning for Virtual Screening
Bowei He, Bowen Gao, Yankai Chen +5
Virtual screening (VS) is an essential task in drug discovery, focusing on the identification of small-molecule ligands that bind to specific protein pockets. Existing deep learnin…
Coder as Editor: Code-driven Interpretable Molecular Editing
Wenyu Zhu, Chengzhu Li, Xiaohe Tian +8
Molecular design is a central task in drug discovery that requires precise structural reasoning and domain knowledge. While large language models (LLMs) have shown promise in gener…
Learning Protein-Ligand Binding in Hyperbolic Space
Jianhui Wang, Wenyu Zhu, Bowen Gao +4
Protein-ligand binding prediction is central to virtual screening and affinity ranking, two fundamental tasks in drug discovery. While recent retrieval-based methods embed ligands…
AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation
Wenyu Zhu, Jianhui Wang, Bowen Gao +5
Virtual screening (VS) is a critical component of modern drug discovery, yet most existing methods--whether physics-based or deep learning-based--are developed around holo protein…
Manipulating 3D Molecules in a Fixed-Dimensional E(3)-Equivariant Latent Space
Zitao Chen, Yinjun Jia, Zitong Tian +2
Medicinal chemists often optimize drugs considering their 3D structures and designing structurally distinct molecules that retain key features, such as shapes, pharmacophores, or c…
Straight-Line Diffusion Model for Efficient 3D Molecular Generation
Yuyan Ni, Shikun Feng, Haohan Chi +5
Diffusion-based models have shown great promise in molecular generation but often require a large number of sampling steps to generate valid samples. In this paper, we introduce a…
UniGEM: A Unified Approach to Generation and Property Prediction for Molecules
Shikun Feng, Yuyan Ni, Yan Lu +3
Molecular generation and molecular property prediction are both crucial for drug discovery, but they are often developed independently. Inspired by recent studies, which demonstrat…
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction
Yuyan Ni, Shikun Feng, Xin Hong +5
Deep learning methods have been considered promising for accelerating molecular screening in drug discovery and material design. Due to the limited availability of labelled data, v…
Multi-level Interaction Modeling for Protein Mutational Effect Prediction
Yuanle Mo, Xin Hong, Bowen Gao +2
Protein-protein interactions are central mediators in many biological processes. Accurately predicting the effects of mutations on interactions is crucial for guiding the modulatio…
Contextual Molecule Representation Learning from Chemical Reaction Knowledge
Han Tang, Shikun Feng, Bicheng Lin +4
In recent years, self-supervised learning has emerged as a powerful tool to harness abundant unlabelled data for representation learning and has been broadly adopted in diverse are…
Equivariant Flow Matching with Hybrid Probability Transport
Yuxuan Song, Jingjing Gong, Minkai Xu +5
The generation of 3D molecules requires simultaneously deciding the categorical features~(atom types) and continuous features~(atom coordinates). Deep generative models, especially…
DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
Bowen Gao, Bo Qiang, Haichuan Tan +6
Virtual screening, which identifies potential drugs from vast compound databases to bind with a particular protein pocket, is a critical step in AI-assisted drug discovery. Traditi…
UniMAP: Universal SMILES-Graph Representation Learning
Shikun Feng, Lixin Yang, Yanwen Huang +3
Molecular representation learning is fundamental for many drug related applications. Most existing molecular pre-training models are limited in using single molecular modality, eit…
ProFSA: Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment
Bowen Gao, Yinjun Jia, Yuanle Mo +4
Pocket representations play a vital role in various biomedical applications, such as druggability estimation, ligand affinity prediction, and de novo drug design. While existing ge…
Multimodal Molecular Pretraining via Modality Blending
Qiying Yu, Yudi Zhang, Yuyan Ni +4
Self-supervised learning has recently gained growing interest in molecular modeling for scientific tasks such as AI-assisted drug discovery. Current studies consider leveraging bot…