Machine Learning-Driven Drug Discovery: Fast Prediction of Binding Property with Molecular Sub-Structures Analysis

Recent advances in deep learning have enabled the screening of drugs from large datasets, the study of new compounds, and the interpretation of the significance of substructures with target characteristics. In this work, we explored in silico analysis by utilizing a convolutional neural network and a transformer in modeling the efficacy of molecules ranging from weak to strong in their binding with sodium ions as an example. We adopted the most representative models, including the VGG19 model and the vision transformer (ViT), trained them on 2D depictions of molecular structures to predict their equilibrium constants, and compared the predictions to the experimental results. VGG19 and ViT both achieved a normalized RMSE of 9% and 8%, respectively, in predicting the equilibrium constant, while also achieving Pearson Correlation Coefficients of 0.80 and 0.83, respectively, between the predicted and experimental results data. This demonstrates the great predictive capacity of well-established computer vision models for this task.Clinical Relevance: This work predicts the binding constants between organic molecules and sodium ions. Identification of key structural properties of the molecules can increase the hit rate in primary drug screening assays.