AI-Optimized Photon-Assisted Molecular Docking for Rapid Drug Discovery

Abstract

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.