# Predicting Aqueous Solubility of Organic Molecules Using Deep Learning Models with Varied Molecular Representations

arXiv: https://arxiv.org/abs/2105.12638 · HTML: https://mlsanity.com/paper/2105.12638/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 95 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [Molecular De Novo Design through Deep Reinforcement Learning](https://mlsanity.com/paper/1704.07555/) (2017)
- [Optimization of Molecules via Deep Reinforcement Learning](https://mlsanity.com/paper/1810.08678/) (2018)
- [Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules](https://mlsanity.com/paper/1910.10685/) (2019)

## Built on by (most cited first, 6 of 6)

- [Application of Graph Neural Networks and graph descriptors for graph classification](https://mlsanity.com/paper/2211.03666/) (2022)
- [Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks](https://mlsanity.com/paper/2307.05318/) (2023)
- [Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models](https://mlsanity.com/paper/2302.06454/) (2023)
- [Enhancing Drug Discovery: Autoencoder-Based Latent Space Augmentation for Improved Molecular Solubility Prediction using LatMixSol](https://mlsanity.com/paper/2506.00223/) (2025)
- [EnSol: an environment-aware graph neural network for molecular solubility prediction](https://mlsanity.com/paper/2609.21151/) (2026)
- [DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction](https://mlsanity.com/paper/2610.02574/) (2026)

## Abstract (arXiv)

Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications. Despite efforts made over decades, there are still challenges associated with developing a solubility prediction model with satisfactory accuracy for many of these applications. The goal of this study is to develop a general model capable of predicting the solubility of a broad range of organic molecules. Using the largest currently available solubility dataset, we implement deep learning-based models to predict solubility from molecular structure and explore several different molecular representations including molecular descriptors, simplified molecular-input line-entry system (SMILES) strings, molecular graphs, and three-dimensional (3D) atomic coordinates using four different neural network architectures - fully connected neural networks (FCNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and SchNet. We find that models using molecular descriptors achieve the best performance, with GNN models also achieving good performance. We perform extensive error analysis to understand the molecular properties that influence model performance, perform feature analysis to understand which information about molecular structure is most valuable for prediction, and perform a transfer learning and data size study to understand the impact of data availability on model performance.