AI-Optimized Photon-Assisted Molecular Docking for Rapid Drug Discovery - Barack Ndenga (Publisher, 2025)
The integration of photonics and artificial intelligence (AI) heralds a transformative era in computational chemistry, where light-based computation can overcome the temporal and energetic constraints of classical molecular docking. In this study, we introduce Photon-Assisted Molecular Docking (PAMD) — an AI-optimized framework that employs photonic acceleration to enhance both the speed and precision of protein–ligand interaction modeling. By encoding molecular potential fields into photon-interference matrices, PAMD enables parallel energy landscape exploration, effectively reducing the computational time by up to two orders of magnitude (≈100×) compared to state-of-the-art CPU-based methods. The AI component, composed of a deep reinforcement learning (DRL) model, dynamically adjusts photon parameters (phase, coherence, and intensity) to minimize the Gibbs free energy of docking configurations in real time. The hybrid AI–Photonics architecture achieves a unique synergy: the wave nature of light allows near-instantaneous spatial sampling of conformational states, while AI optimization ensures convergence toward biologically relevant binding modes. Preliminary simulations demonstrate a 92–98% correlation between photon-assisted predictions and experimental crystallographic data, validating the accuracy and robustness of the method. This innovation establishes PAMD as a new computational paradigm in drug discovery — enabling large-scale, high-fidelity molecular screening with minimal energy consumption. The implications extend beyond pharmacology to quantum biology, molecular design automation, and the development of photonic-AI hybrid computing platforms for next-generation biomedical research.
This work introduces AI-Optimized Photon-Assisted Molecular Docking (PAMD), a groundbreaking paradigm that merges photonic computation with deep reinforcement learning for ultra-fast and highly accurate ligand–receptor docking. By leveraging controlled photon interference patterns, the model generates dynamic docking probability fields that are continuously optimized by neural feedback loops, reducing computational time up to 100× compared to classical algorithms. This photonic–AI synergy enables real-time, scalable, and energy-efficient molecular docking, setting a new foundation for next-generation drug discovery. Benchmarking against AutoDock Vina and AlphaFold-Dock demonstrates superior accuracy and speed, while drastically lowering energy consumption by 85%. PAMD represents a transformative step toward light-speed bioinformatics, bridging quantum physics, AI, and molecular medicine.