AI-Driven Virtual Screening of Naturalcompounds Against SARS-CoV-2 Using Embedding-Based Drug-Protein Interaction Prediction

Abstract

The imperative necessity for rapid discovery of antiviral agents against emerging viral diseases, such as COVID-19 caused by SARS-CoV-2, has emphasised the limitations of conventional drug discovery, with its deliberate pace, high costs, and high failure rate. In this study, we present a high-throughput “AI-driven virtual screening pipeline” that combines cutting-edge molecular embeddings using transformers for compounds and language model-based protein sequences for targets, and combines these using a gradient-boosted decision tree-based model (XGBoost) trained on ChEMBL bioactivity data, with excellent performance for binary prediction (ROC-AUC 0.8408, Accuracy 0.76) comparable to top-performing methods. When applied to the large library of natural products from COCONUT database, our model correctly predicted top-ranked compounds, some of which were shortlisted for molecular docking against the SARS-CoV-2 spike receptor-binding domain (RBD), the key interface for ACE2 binding, in order to test the validity of proposed pipeline and the results revealed several promising compounds with excellent binding energies and multiple interactions with hotspot residues such as K417, Y453, Q493, G496, Q498, N501, Y505 and F486. The potential of cost-effective, synergistic integration of scalable deep learning of representations, interpretable machine learning, and physics-based refinement of structure is significant for accelerating natural product-based therapeutics for coronaviruses and possible other viral threats, with the potential to expand to ensemble approaches, active learning, and variant-based targets for enhanced efficacy.