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Found 10 papers, 4 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.
Large Language Model as Universal Retriever in Industrial-Scale Recommender System
In real-world recommender systems, different retrieval objectives are typically addressed using task-specific datasets with carefully designed model architectures.
Dynamic Attention-Guided Context Decoding for Mitigating Context Faithfulness Hallucinations in Large Language Models
Large language models (LLMs) often suffer from context faithfulness hallucinations, where outputs deviate from retrieved information due to insufficient context utilization and high output uncertainty.
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis
We believe this dataset will serve as a more accurate and reliable benchmark for molecular representation learning, thereby expediting progress in the field of artificial intelligence-driven drug discovery.
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.
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning
Recently, a noticeable trend has emerged in developing pre-trained foundation models in the domains of CV and NLP.
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.
UniMAP: Universal SMILES-Graph Representation Learning
Molecular representation learning is fundamental for many drug related applications.
