arXiv (All)
open access
EnSol: an environment-aware graph neural network for molecular solubility prediction
Molecular solubility directly affects key aspects of molecular development such as reaction feasibility, formulation performance, separation efficiency, and solvent selection. However, experimental measurement across solutes, solvents, and temperatures remains costly and sparsely sampled. Existing c
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
- Advancing Aqueous Solubility Prediction: A Machine Learning Approach for Organic Compounds Using a Curated Data Set.#442924
- Chemical-space completeness through iterative crystal-structure generation and model adaptation#1117277
- Role-Aware Morgan Fingerprints for Reaction Yield Prediction#1036850
- SynRXN: An Open Benchmark and Curated Dataset for Computational Reaction Modeling.#122774
- CoMPASS: Collaborative Molecular Property Prediction via Adaptive Small-Large Model Synergy#818729
Record · ID 1035119
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