Decoding the protein–ligand interactions using parallel graph neural networks

Protein-ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: Formula: see text is the base implementation that employs distinct featurization to enhance domain-awareness, while Formula: see text is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein's 3D structure with 0.979 test accuracy for Formula: see text and 0.958 for Formula: see text for predicting activity of a protein-ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and Formula: see text crucial for compound's potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on Formula: see text with Formula: see text and Formula: see text, respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of Formula: see text on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.