Attention-Based Graph Neural Network for Molecular Solubility Prediction

Drug discovery (DD) research is aimed at the discovery of new medications. Solubility is an important physicochemical property in drug development. Active pharmaceutical ingredients (APIs) are essential substances for high drug efficacy. During DD research, aqueous solubility (AS) is a key physicochemical attribute required for API characterization. High-precision in silico solubility prediction reduces the experimental cost and time of drug development. Several artificial tools have been employed for solubility prediction using machine learning and deep learning techniques. This study aims to create different deep learning models that can predict the solubility of a wide range of molecules using the largest currently available solubility data set. Simplified molecular-input line-entry system (SMILES) strings were used as molecular representation, models developed using simple graph convolution, graph isomorphism network, graph attention network, and AttentiveFP network. Based on the performance of the models, the AttentiveFP-based network model was finally selected. The model was trained and tested on 9943 compounds. The model outperformed on 62 anticancer compounds with metric Pearson correlation R-2 and root-mean-square error values of 0.52 and 0.61, respectively. AS can be improved by graph algorithm improvement or more molecular properties addition.

Automated summary

Drug discovery (DD) research aims to discover new medications. Solubility is an important property in drug development. Aqueous solubility (AS) is a key attribute required for API characterization. In this study, deep learning models were created to predict the solubility of a wide range of molecules using the largest currently available solubility data set. The models were trained and tested on 9943 compounds, with the AttentiveFP-based network model outperforming on 62 anticancer compounds.