arXiv (OAI Expanded)
open access
MIRAGE: Measuring Interpolation and Redundancy in Affinity GEneralization
Deep learning now underpins structure-based drug design, from complex and affinity prediction to ligand ranking and pose generation. Recent co-folding models reportedly approach free-energy-perturbation accuracy at far lower cost. Yet standard evaluation, a single held-out correlation or pooled pose
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
- Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning#243137
- FLOWR.ROOT – A flow matching-based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction#485073
- 3DICE: interpretable 3D cross-modal learning for drug-target interaction prediction and large-scale drug discovery.#388748
- DrugDL: dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery.#342477
- Official Review · ICLR 2025#787137
Record · ID 1083922
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