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

arXiv: https://arxiv.org/abs/2506.00223 · HTML: https://mlsanity.com/paper/2506.00223/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 58 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 12 of 12)

- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Conditional molecular design with deep generative models](https://mlsanity.com/paper/1805.00108/) (2018)
- [Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective](https://mlsanity.com/paper/2103.00397/) (2021)
- [Predicting Aqueous Solubility of Organic Molecules Using Deep Learning Models with Varied Molecular Representations](https://mlsanity.com/paper/2105.12638/) (2021)
- [Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning](https://mlsanity.com/paper/2210.07145/) (2022)
- [Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks](https://mlsanity.com/paper/2307.05318/) (2023)
- [Predicting Drug Solubility Using Different Machine Learning Methods -- Linear Regression Model with Extracted Chemical Features vs Graph Convolutional Neural Network](https://mlsanity.com/paper/2308.12325/) (2023)
- [Interpolatory model reduction of dynamical systems with root mean squared error](https://mlsanity.com/paper/2403.08894/) (2024)

## Abstract (arXiv)

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.