AI Model for Predicting Binding Affinity of Antidiabetic Compounds Targeting PPAR

TL;DR

This paper presents a deep learning model trained on molecular descriptors and docking data to predict binding affinities of compounds targeting PPAR, aiding drug discovery for antidiabetic therapies.

Contribution

It introduces a novel deep learning approach combining 2D descriptors and docking simulations for accurate affinity prediction targeting PPAR.

Findings

Model achieved R-squared of 0.861 on training data

Model achieved R-squared of 0.655 on test data

Demonstrates potential for accelerating PPAR-targeted drug discovery

Abstract

This study aims to develop a deep learning model for predicting the binding affinity of ligands targeting the Peroxisome Proliferator-Activated Receptor (PPAR) family, using 2D molecular descriptors. A dataset of 3,764 small molecules with known binding affinities, sourced from the ChEMBL database, was preprocessed by eliminating duplicates and incomplete data. Molecular docking simulations using AutoDock Vina were performed to predict binding affinities for the PPAR receptor family. 2D molecular descriptors were computed from the SMILES notation of each ligand, capturing essential structural and physicochemical features. These descriptors, along with the predicted binding affinities, were used to train a deep learning model to predict binding affinity as a regression task. The model was evaluated using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared. Results indicated strong performance with an R squared value of 0.861 for the training set and 0.655 for the test set, suggesting good model generalization. The model shows promise for predicting ligand-receptor interactions and can be applied in drug discovery efforts targeting PPAR-related diseases.