# A Unified Approach to Inferring Chemical Compounds with the Desired Aqueous Solubility

arXiv: https://arxiv.org/abs/2409.04301 · HTML: https://mlsanity.com/paper/2409.04301/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 6 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [A Novel Method for Inference of Acyclic Chemical Compounds with Bounded Branch-height Based on Artificial Neural Networks and Integer Programming](https://mlsanity.com/paper/2009.09646/) (2020)
- [Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search](https://mlsanity.com/paper/2108.10266/) (2021)
- [A Method for Inferring Polymers Based on Linear Regression and Integer Programming](https://mlsanity.com/paper/2109.02628/) (2021)

## Abstract (arXiv)

Aqueous solubility (AS) is a key physiochemical property that plays a crucial role in drug discovery and material design. We report a novel unified approach to predict and infer chemical compounds with the desired AS based on simple deterministic graph-theoretic descriptors, multiple linear regression (MLR) and mixed integer linear programming (MILP). Selected descriptors based on a forward stepwise procedure enabled the simplest regression model, MLR, to achieve significantly good prediction accuracy compared to the existing approaches, achieving the accuracy in the range [0.7191, 0.9377] for 29 diverse datasets. By simulating these descriptors and learning models as MILPs, we inferred mathematically exact and optimal compounds with the desired AS, prescribed structures, and up to 50 non-hydrogen atoms in a reasonable time range [6, 1204] seconds. These findings indicate a strong correlation between the simple graph-theoretic descriptors and the AS of compounds, potentially leading to a deeper understanding of their AS without relying on widely used complicated chemical descriptors and complex machine learning models that are computationally expensive, and therefore difficult to use for inference. An implementation of the proposed approach is available at https://github.com/ku-dml/mol-infer/tree/master/AqSol.