Graph Neural Network Framework for Web-Based Prediction of Protein-Ligand Docking Scores across multiple organs v1
Anagha Shamsundar Setlur, Vidya Niranjan, Arjun Balaji, Chandrashekar K · 2023
Estimating the docking score between proteins and drugs is very important in the application of structure-based drug design. This project explores the application of Graph Neural networks (GNN) in the field of molecular property prediction using SMILES representation, the trained models are then deployed on a web-based platform for broader accessibility and use. The primary dataset utilized in this study includes molecular data represented by MolPort IDs and associated docking scores, which are critical in assessing molecular interactions. A significant aspect of this project is data preprocessing, where each molecule, initially represented as a SMILES string, is converted into a graph format. Effective molecular representation learning is pivotal to facilitate molecular property prediction. Models are then evaluated based on various performance metrics and deployed on the web-based platform. Keywords: QSAR, machine and deep learning, graph convolution networks, graph neural networks, data pre-processing, human organs, web-based predictions