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Welcome to the Solvation wiki!

Reviews of the state of the art:

• Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction

• SolvBERT for solvation free energy and solubility prediction: a demonstration of an NLP model for predicting the properties of molecular complexes

• Accurate Prediction of Aqueous Free Solvation Energies Using 3D Atomic Feature-Based Graph Neural Network with Transfer Learning

• Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction

• Accurate predictions of aqueous solubility of drug molecules via the multilevel graph convolutional network (MGCN) and SchNet architectures

• Attention-Based Graph Neural Network for Molecular Solubility Prediction

• Delfos: deep learning model for prediction of solvation free energies in generic organic solvents

• Prediction of small‑molecule compound solubility in organic solvents by machine learning algorithms

• MLSolvA: solvation free energy prediction from pairwise atomistic interactions by machine learning

• Machine learning with physicochemical relationships: solubility prediction in organic solvents and water

• Prediction of the solubility of organic compounds in high-temperature water using machine learning

• Multiple machine learning algorithms assisted QSPR models for aqueous solubility: Comprehensive assessment with CRITIC-TOPSIS

Models (Solubility Prediction):

• AquaPred (GNN)
• solubility-prediction-paper (GNN, MDM, SCH, SMI)

Models (Solvation Energy Prediction):

• SolvBERT
• mlsolva
• solvation_energy_prediction

Data:

• BNNLab: Solubility data
• Deep Learning Meets Chemistry

Online databases:

• NIST Solubility
• IUPAC SOLUBILITY DATA SERIES
• AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds
• data from aqsoldb
• Minnesota Solvation Database
• Open Notebook Science Challenge Solubility Dataset
• Handbook of Aqueous Solubility Data
• BigSolDB