drug discovery
Drug discovery is the process through which novel pharmaceutical compounds are identified and developed. AI techniques are increasingly utilized in this domain to predict molecular activity, optimize lead compounds, and analyze biological data, dramatically accelerating development timelines.
- 3D Interaction Geometric Pre-training for Molecular Relational Learning
- AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation
- Automatic Auxiliary Task Selection and Adaptive Weighting Boost Molecular Property Prediction
- Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization
- DecoyDB: A Dataset for Graph Contrastive Learning in Protein-Ligand Binding Affinity Prediction
- EDBench: Large-Scale Electron Density Data for Molecular Modeling
- Enhancing Bioactivity Prediction via Spatial Emptiness Representation of Protein-ligand Complex and Union of Multiple Pockets
- FGBench: A Dataset and Benchmark for Molecular Property Reasoning at Functional Group-Level in Large Language Models
- Iterative Foundation Model Fine-Tuning on Multiple Rewards
- JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensemble Generation
- Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model
- NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models
- OligoGym: Curated Datasets and Benchmarks for Oligonucleotide Drug Discovery
- Towards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion Modeling
- Towards precision protein-ligand affinity prediction benchmark: A Complete and Modification-Aware DAVIS Dataset
- UMA: A Family of Universal Models for Atoms