Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction.

ABSTRACT: 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 goals of this study are to assess current deep learning methods for solubility prediction, develop a general model capable of predicting the solubility of a broad range of organic molecules, and to understand the impact of data properties, molecular representation, and modeling architecture on predictive performance. Using the largest currently available solubility data set, we implement deep learning-based models to predict solubility from the molecular structure and explore s

SUBMITTER: Panapitiya G 

PROVIDER: S-EPMC9096921 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature