Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility

The solubility of chemical substances in water is a critical
parameter
in pharmaceutical development, environmental chemistry, agrochemistry,
and other fields; however, accurately predicting it remains a challenge.
This study aims to evaluate and compare the effectiveness of some
of the most popular machine learning modeling methods and molecular
featurization techniques in predicting aqueous solubility. Although
these methods were not implemented in a competitive environment, some
of their performance surpassed previous benchmarks, offering gradual
but significant improvements. Our results show that methods based
on graph convolution and graph attention mechanisms demonstrated exceptional
predictive abilities with high-quality data sets, albeit with a sensitivity
to data noise and errors. In contrast, models leveraging molecular
descriptors not only provided better interpretability but also showed
more resilience when dealing with inherent noise and errors in data.
Our analysis of over 4000 molecular descriptors used in various models
identified that approximately 800 of these descriptors make a significant
contribution to solubility prediction. These insights offer guidance
and direction for future developments in solubility prediction.