iScore: A ML-Based Scoring Function for de novo Drug Discovery

Abstract

In the quest for accelerating de novo drug discovery, the development of efficient and accurate scoring functions represents a fundamental challenge. This study introduces iScore, a novel machine learning (ML)-based scoring function designed to predict the binding affinity of protein-ligand complexes with remarkable speed and precision. Uniquely, iScore circumvents the conventional reliance on explicit knowledge of protein-ligand interactions and full picture of atomic contacts, instead leveraging a set of ligand and binding pocket descriptors to evaluate binding affinity. This approach avoids the inefficient and slow conformational sampling stage, thereby enabling the rapid screening of ultra-huge molecular libraries, a crucial advancement given the practically infinite dimensions of chemical space. iScore was rigorously trained and validated using the PDBbind 2020 refined set, CASF 2016, and CSAR NRC-HiQ Set1/2, employing three distinct ML methodologies: Deep Neural Network (iScore-DNN), Random Forest (iScore-RF), and eXtreme Gradient Boosting (iScore-XGB). A hybrid model, iScore-Hybrid, was subsequently developed to incorporate the strengths of these individual base learners. The hybrid model demonstrated a Pearson correlation coefficient (R) of 0.78 and a root mean square error (RMSE) of 1.23 in cross-validation, outperforming the individual base learners and establishing new benchmarks for scoring power (R = 0.814, RMSE=1.34), ranking power ({rho} = 0.705), and screening power (success rate at top 10% = 73.7%).