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Found 12 papers, 5 papers with code
PharmAgents: Building a Virtual Pharma with Large Language Model Agents
The discovery of novel small molecule drugs remains a critical scientific challenge with far-reaching implications for treating diseases and advancing human health.
Pushing the boundaries of Structure-Based Drug Design through Collaboration with Large Language Models
Structure-Based Drug Design (SBDD) has revolutionized drug discovery by enabling the rational design of molecules for specific protein targets.
From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics
Recent advancements in structure-based drug design (SBDD) have significantly enhanced the efficiency and precision of drug discovery by generating molecules tailored to bind specific protein pockets.
SIU: A Million-Scale Structural Small Molecule-Protein Interaction Dataset for Unbiased Bioactivity Prediction
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-saving therapeutics.
Multi-level Interaction Modeling for Protein Mutational Effect Prediction
Protein-protein interactions are central mediators in many biological processes.
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion
In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score.
Delta Score: Improving the Binding Assessment of Structure-Based Drug Design Methods
Structure-based drug design (SBDD) stands at the forefront of drug discovery, emphasizing the creation of molecules that target specific binding pockets.
ProFSA: Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment
Pocket representations play a vital role in various biomedical applications, such as druggability estimation, ligand affinity prediction, and de novo drug design.
DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
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.
UniBriVL: Robust Universal Representation and Generation of Audio Driven Diffusion Models
Multimodal large models have been recognized for their advantages in various performance and downstream tasks.
Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D
Generating desirable molecular structures in 3D is a fundamental problem for drug discovery.
Exploring Efficient-Tuned Learning Audio Representation Method from BriVL
Recently, researchers have gradually realized that in some cases, the self-supervised pre-training on large-scale Internet data is better than that of high-quality/manually labeled data sets, and multimodal/large models are better than single or bimodal/small models.
