8 citations · 28 across the 21 of their papers we have counts for
24 papers
Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction
Mianzhi Pan, JianFei Li, Peishuo Liu +5
Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures rem…
FLEX: Continuous Agent Evolution via Forward Learning from Experience
Zhicheng Cai, Xinyuan Guo, Yu Pei +7
Autonomous agents driven by Large Language Models (LLMs) have revolutionized reasoning and problem-solving but remain static after training, unable to grow with experience as intel…
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…
Revisiting Sampling Strategies for Molecular Generation
Yuyan Ni, Shikun Feng, Wei-Ying Ma +2
Sampling strategies in diffusion models are critical to molecular generation yet remain relatively underexplored. In this work, we investigate a broad spectrum of sampling methods…
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…
PharmAgents: Building a Virtual Pharma with Large Language Model Agents
Bowen Gao, Yanwen Huang, Yiqiao Liu +4
The discovery of novel small molecule drugs remains a critical scientific challenge with far-reaching implications for treating diseases and advancing human health. Traditional dru…
Pushing the boundaries of Structure-Based Drug Design through Collaboration with Large Language Models
Bowen Gao, Yanwen Huang, Yiqiao Liu +4
Structure-Based Drug Design (SBDD) has revolutionized drug discovery by enabling the rational design of molecules for specific protein targets. Despite significant advancements in…
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…
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis
Shikun Feng, Jiaxin Zheng, Yinjun Jia +4
Molecular representation learning is pivotal for various molecular property prediction tasks related to drug discovery. Robust and accurate benchmarks are essential for refining an…
From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics
Bowen Gao, Haichuan Tan, Yanwen Huang +5
Recent advancements in structure-based drug design (SBDD) have significantly enhanced the efficiency and precision of drug discovery by generating molecules tailored to bind specif…
SIU: A Million-Scale Structural Small Molecule-Protein Interaction Dataset for Unbiased Bioactivity Prediction
Yanwen Huang, Bowen Gao, Yinjun Jia +4
Small molecules play a pivotal role in modern medicine, and scrutinizing their interactions with protein targets is essential for the discovery and development of novel, life-savin…
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning
Shikun Feng, Yuyan Ni, Minghao Li +4
Recently, a noticeable trend has emerged in developing pre-trained foundation models in the domains of CV and NLP. However, for molecular pre-training, there lacks a universal mode…
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion
Bowen Gao, Minsi Ren, Yuyan Ni +5
In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score. However, further study sh…
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…
Protein-ligand binding representation learning from fine-grained interactions
Shikun Feng, Minghao Li, Yinjun Jia +2
The binding between proteins and ligands plays a crucial role in the realm of drug discovery. Previous deep learning approaches have shown promising results over traditional comput…
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method
Yuyan Ni, Shikun Feng, Wei-Ying Ma +2
While molecular pre-training has shown great potential in enhancing drug discovery, the lack of a solid physical interpretation in current methods raises concerns about whether the…
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…