The accurate prediction of drug molecule solubility is essential for
determining their therapeutic effectiveness and safety, influencing the drug's
ADME processes. Traditional solubility prediction techniques often fail to
capture the complex nature of molecular tructures, leading to notable
deviations between predictions and actual results. For example, the Discussion
on Advanced Drug-Like Compound Structures. Lusci highlighted issues in
capturing crucial cyclic structural information in molecules with ring
structures. To overcome this issue, our research introduces a novel deep
learning framework combining attention-based transformers, Long Short-Term
Memory (LSTM) networks, and Graph Convolutional Networks (GCN), aimed at
enhancing the precision of solubility predictions. Utilizing a training set of
9,943 compounds and testing on an anticancer compound dataset, our method
achieved a correlation coefficient ($R^2$) of 0.55 and a Root Mean Square Error
(RMSE) of 0.59, which outperforms the benchmark models' scores of 0.52 ($R^2$)
and 0.61 (RMSE). Importantly, in an additional independent test, our model
significantly outperformed the baseline with an RMSE of 1.05 compared to 1.28,
a relative accuracy improvement of 45.9%. This research not only demonstrates
the vast potential of deep learning for improving solubility prediction
accuracy but also offers novel insights for drug design and selection in the
future. Continued efforts will be directed towards optimizing the model
architecture and extending its application to better support the drug
development process, underscoring the pivotal role of deep learning in drug
discovery.

通过引入深度学习框架，结合基于注意力机制的转换器、长短期记忆神经网络和图卷积网络，我们的研究旨在提高溶解度预测的准确性，为药物设计和选择提供新的见解和支持，为药物发现中的深度学习发挥重要作用展示了巨大潜力。