GGAS2SN: Gated Graph and SmilesToSeq Network for Solubility Prediction.

Area of Science:

• Computational Chemistry
• Cheminformatics
• Drug Discovery

Background:

• Aqueous solubility is a critical physicochemical property influencing drug absorption and efficacy.
• Accurate solubility prediction is vital for pharmaceutical development, environmental studies, and pharmacological research.
• Existing methods often struggle to capture complex molecular interactions influencing solubility.

Purpose of the Study:

• To develop a novel, highly accurate method for predicting aqueous solubility.
• To integrate graph neural networks with advanced encoding techniques for improved molecular representation.
• To provide interpretable insights into molecular features that govern solubility.

Main Methods:

• Chemical compounds were converted into graph structures (atoms as nodes, bonds as edges).
• A specialized graph neural network (GNN) architecture, combining gated graph neural networks (GGNNs) and graph attention neural networks (GATs), was employed.
• Smiles2Seq encoding was utilized to translate molecular structures into numerical sequences for model input.

Main Results:

• The proposed model demonstrated superior predictive performance compared to traditional solubility prediction methods.
• Experiments on benchmark datasets confirmed the model's efficacy and robustness.
• The approach provided interpretable insights into molecular features driving solubility behavior.

Conclusions:

• The fusion of GGNN, GAT, and Smiles2Seq encoding offers a powerful framework for accurate solubility prediction.
• This advancement provides valuable tools for accelerating drug discovery, optimizing formulation development, and supporting environmental assessments.
• The developed method represents a significant step forward in computational prediction of physicochemical properties.