Graph-Based Approach for Solubility Prediction of Drugs using SMILES Data
Keywords:
GCN, GNN, Grid Hyperparameter Optimization, RMSE, SMILESAbstract
Graph Neural Networks (GNN) utilization in the case of molecular property prediction is considered a significant advancement in computational chemistry and drug discovery. Initial approaches to molecular property prediction especially solubility prediction depend on empirical rules or physicochemical descriptors, which lack generalization and predictive accuracy. The proposed model Graph Convolutional Network (GCN) which is a variant of GNN learns representations of molecular graphs, enabling accurate prediction of molecular properties directly from raw molecular structures. The molecular graphs are created from the Simplified Molecular Input Line Entry System (SMILES) data which are molecular sequences of drug target compounds. In the proposed work, GCN uses graph pooling, which effectively reduces the node dimensionality. This work shows how the whole graph can be considered as input and how different pooling techniques can be used to handle large and complex graph data and also the effectiveness of GCN for solubility prediction. The proposed GCN model is hyperparameter tuned by using Grid Hyperparameter optimization on ESoL dataset which is a regressive type dataset achieving a low RMSE value of 0.43 outperforming machine learning and many deep learning models.
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