用SMILES字符串预测高分子在溶剂中的溶解度，支持绿色化学设计。
Predicting Polymer Solubility in Solvents Using SMILES Strings
- 基于聚合物和溶剂的SMILES编码，构建深度学习模型直接预测溶解度。
- 在8049组数据上训练，模型在25℃下预测误差小，实验数据验证准确率高。
- 适用于高通量溶剂筛选，助力环保材料与制药研发。
理解并预测高分子在不同溶剂中的溶解度对回收利用到药物制剂等应用至关重要。本文提出一种深度学习框架，直接从聚合物和溶剂的SMILES表示中预测溶解度（以重量百分比wt%表示）。研究基于周等人（2023）的校准分子动力学模拟，构建了8,049组25℃下的聚合物-溶剂对数据集，并将分子描述符与指纹融合为每样本2,394维特征向量。采用含六层隐藏层的全连接神经网络，使用Adam优化器和均方误差损失进行训练，预测值与实测值高度一致。通过材料基因组计划的实验数据验证，模型在25组未见过的组合上仍保持高精度。结果表明，基于SMILES的机器学习模型可实现可扩展的溶解度预测与高通量溶剂筛选，适用于绿色化学、高分子加工与材料设计。
原文摘要 · Abstract (English)
Understanding and predicting polymer solubility in various solvents is critical for applications ranging from recycling to pharmaceutical formulation. This work presents a deep learning framework that predicts polymer solubility, expressed as weight percent (wt%), directly from SMILES representations of both polymers and solvents. A dataset of 8,049 polymer solvent pairs at 25 deg C was constructed from calibrated molecular dynamics simulations (Zhou et al., 2023), and molecular descriptors and fingerprints were combined into a 2,394 feature representation per sample. A fully connected neural network with six hidden layers was trained using the Adam optimizer and evaluated using mean squared error loss, achieving strong agreement between predicted and actual solubility values. Generalizability was demonstrated using experimentally measured data from the Materials Genome Project, where the model maintained high accuracy on 25 unseen polymer solvent combinations. These findings highlight the viability of SMILES based machine learning models for scalable solubility prediction and high-throughput solvent screening, supporting applications in green chemistry, polymer processing, and materials design.
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