Spectral analysis and AI/ML-based data-driven approaches for enhanced protein conformation selection and prediction in drug discovery
Artificial intelligence (AI) has become an important tool in drug discovery by enabling the analysis of large-scale molecular dynamics (MD) simulation data and improving the understanding of protein-ligand interactions. However, identifying functionally relevant ligand-binding conformations from highly dynamic MD trajectories remains a major challenge. We propose a spectral analysis-based AI/machine learning (ML) framework to improve the identification of ligand-binding protein conformations. The framework compares the Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) by transforming protein feature time series into frequency-domain and time-frequency-domain representations, respectively. These spectral features capture global conformational dynamics and localized structural changes. A probabilistic majority-voting decision-fusion strategy integrates predictions from multiple AI/ML models, while the spectral feature space is used to mitigate class imbalance, improve the signalto-noise ratio, and enhance discriminative learning. The framework was evaluated on three G protein-coupled receptors (GPCRs): ADORA2A, OPRD1, and OPRK1. Compared with baseline models trained on raw time-series data, the proposed approach achieved improved predictive performance. FFT-based features effectively captured high-frequency conformational signatures, whereas DWT-based features identified localized high-energy patterns associated with ligand-binding events. The decision-fusion strategy further improved the sensitivity, the area under the receiver operating characteristic curve (AUC), and the overall consistency of the prediction across all targets. Spectral-domain analysis substantially enhances AI-driven identification of ligand-binding protein conformations by providing complementary representations of protein dynamics and improving classification performance. This framework offers a robust approach for analyzing MD simulations in the discovery of structure-based drugs and can facilitate the identification of biologically relevant conformations. Future work will validate the predicted conformations through molecular docking and extend the framework to additional therapeutic protein targets.