license: mit
library_name: chemprop
tags:
- chemistry
- thermodynamics
- melting-point
- enthalpy-of-fusion
- solubility
- chemprop
- screening-properties
Fusion-Cycle: melting temperature and enthalpy of fusion
These are two five-model Chemprop 2 ensembles from Fusion-Cycle:
trained_models/MP: melting temperature, in Ktrained_models/dHfus: enthalpy of fusion, in kcal/mol
Both take a SMILES string as their only input. The files are byte-identical copies from the upstream repository. They are re-hosted here so that screening-properties (the property package of the ScreeningTool process-screening framework) can download them on first use. The solubility (γ) and mixture models of Fusion-Cycle are not included.
All credit goes to the original authors.
- Code and weights: emadalibrahim/Fusion-Cycle, commit
55d8ca577ac1cb5aeea2685523223bd5c9c19977. - Licence: the upstream package metadata (its conda recipe) declares MIT. The upstream repository contains no separate LICENSE file. If you are an author and want this re-hosting changed, please open a discussion on this repository.
Al Ibrahim, Emad, et al. "Accurately predicting solubility curves via a thermodynamic cycle, machine learning, and solvent ensembles." Journal of the American Chemical Society 147.49 (2025): 45057-45069.
Use
from chemprop import models
mpnn = models.MPNN.load_from_file("trained_models/MP/model_0.pt") # pickle: only load files you have verified
screening-properties averages the five members, converts ΔHfus to J/mol (× 4184), and checks every file's SHA-256 before loading. It does not predict molecules without carbon or with more than one fragment (salts), because those lie outside the organic training domain.