Integrated machine learning and molecular dynamics framework for predicting and elucidating ABCB1 allocrite interactions.

Abstract

ABCB1, a polyspecific efflux transporter, mediates multidrug resistance in cancer by interacting with diverse substrates and inhibitors, yet its recognition mechanisms remain elusive. Here, we introduce an integrated framework that synergistically combines biophysics and computational biology to predict ABCB1 allocrite interactions and elucidate their mechanisms. We curated hierarchical-confidence bioactivity datasets from multi-source assays and developed MolMM, a convolutional neural network leveraging meta-learning on noisy data and multi-task learning on refined data, achieving AUC-ROC scores of 83.33% for inhibitors and 81.26% for substrates. SHapley Additive exPlanations (SHAP) analysis revealed key molecular features, highlighting competitive polar, and hydrophobic motifs distinguishing substrates from inhibitors . Building on these ML insights, coarse-grained umbrella sampling simulations mapped these features onto free energy landscapes, proposing an amphiphilic model for substrate binding via a flip-flop process through the transmembrane pore and an inhibitory mechanism stabilizing ABCB1 in transitional conformations at the cavity's gate. This machine learning-molecular dynamics synergy offers mechanistic insights into ABCB1 polyspecificity, facilitating rational design of inhibitors to overcome multidrug resistance.