  Accurate prediction of drug molecule solubility is crucial for therapeutic
effectiveness and safety. Traditional methods often miss complex molecular
structures, leading to inaccuracies. We introduce the YZS-Model, a deep
learning framework integrating Graph Convolutional Networks (GCN), Transformer
architectures, and Long Short-Term Memory (LSTM) networks to enhance prediction
precision. GCNs excel at capturing intricate molecular topologies by modeling
the relationships between atoms and bonds. Transformers, with their
self-attention mechanisms, effectively identify long-range dependencies within
molecules, capturing global interactions. LSTMs process sequential data,
preserving long-term dependencies and integrating temporal information within
molecular sequences. This multifaceted approach leverages the strengths of each
component, resulting in a model that comprehensively understands and predicts
molecular properties. Trained on 9,943 compounds and tested on an anticancer
dataset, the YZS-Model achieved an $R^2$ of 0.59 and an RMSE of 0.57,
outperforming benchmark models ($R^2$ of 0.52 and RMSE of 0.61). In an
independent test, it demonstrated an RMSE of 1.05, improving accuracy by 45.9%.
The integration of these deep learning techniques allows the YZS-Model to learn
valuable features from complex data without predefined parameters, handle large
datasets efficiently, and adapt to various molecular types. This comprehensive
capability significantly improves predictive accuracy and model
generalizability. Its precision in solubility predictions can expedite drug
development by optimizing candidate selection, reducing costs, and enhancing
efficiency. Our research underscores deep learning's transformative potential
in pharmaceutical science, particularly for solubility prediction and drug
design.
