arXiv (All)
open access
Curriculum Learning for Generative Flexible Docking
Flexible docking is a highly practical task, aiming to predict the joint configuration of a protein-ligand complex from a protein apo structure and a ligand SMILES string. Diffusion and flow-based generative models have made this task tractable by modeling the posterior distribution over the ligand
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Harmonic Torsional Diffusion for Protein-Ligand Flexible Docking#658945
- Harmonic Torsional Diffusion for Protein-Ligand Flexible Docking#656809
- Computational Study on Protein Docking with Vascular Disease Application#300475
- Comparative Assessment of DNA Force Fields for Small-Molecule Ligand Binding via Multicanonical MD-Based Dynamic Docking Simulations#398578
- CHARMM-GUI Ligand Docker for Molecular Docking with Various Docking Programs.#14939
Record · ID 1412117
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive