标题： 提升药物发现：基于自动编码器的潜在空间增强用于改进分子溶解度预测的LatMixSol方法 显示英文标题

标题： Enhancing Drug Discovery: Autoencoder-Based Latent Space Augmentation for Improved Molecular Solubility Prediction using LatMixSol

摘要： 分子溶解度的准确预测是早期药物发现的基础，然而，由于标记数据有限以及分子描述符的高维特性，传统的机器学习模型面临重大挑战。 为了解决这些问题，我们提出了 LatMixSol，这是一种结合基于自动编码器的特征压缩与引导插值的新型潜在空间增强框架，以丰富训练数据。 我们的方法首先使用两层自动编码器将分子描述符编码到低维潜在空间中。 然后应用光谱聚类将化学上相似的分子分组，从而在集群内实现目标性的 MixUp 风格插值。 通过混合集群成员的潜在向量并解码回原始特征空间来生成合成样本。 在 Huuskonen 溶解度基准数据集上的评估显示，LatMixSol 在四种梯度提升回归器（CatBoost、LightGBM、HistGradientBoosting）中的三种上表现出一致的改进，实现了 RMSE 减少 3.2%-7.6%，R-squared 提升 0.5%-1.5%。 值得注意的是，HistGradientBoosting 显示出最显著的提升，RMSE 改善了 7.6%。 我们的分析证实，集群引导的潜在空间增强在扩展数据集多样性的同时保留了化学有效性，提供了一种计算高效的策略，可在资源受限的药物发现管道中增强预测模型。 显示英文摘要

摘要： Accurate prediction of molecular solubility is a cornerstone of early-stage drug discovery, yet conventional machine learning models face significant challenges due to limited labeled data and the high-dimensional nature of molecular descriptors. To address these issues, we propose LatMixSol, a novel latent space augmentation framework that combines autoencoder-based feature compression with guided interpolation to enrich training data. Our approach first encodes molecular descriptors into a low-dimensional latent space using a two-layer autoencoder. Spectral clustering is then applied to group chemically similar molecules, enabling targeted MixUp-style interpolation within clusters. Synthetic samples are generated by blending latent vectors of cluster members and decoding them back to the original feature space. Evaluated on the Huuskonen solubility benchmark, LatMixSol demonstrates consistent improvements across three of four gradient-boosted regressors (CatBoost, LightGBM, HistGradientBoosting), achieving RMSE reductions of 3.2-7.6% and R-squared increases of 0.5-1.5%. Notably, HistGradientBoosting shows the most significant enhancement with a 7.6% RMSE improvement. Our analysis confirms that cluster-guided latent space augmentation preserves chemical validity while expanding dataset diversity, offering a computationally efficient strategy to enhance predictive models in resource-constrained drug discovery pipelines.