Photonics + AI for Real-Time Molecular Interaction Mapping

Abstract

This paper introduces a breakthrough in molecular biophysics through the integration of photonics and artificial intelligence for real-time mapping of molecular interactions. The proposed system, AI-driven photonic interaction mapping (AI-PIM), enables the detection, interpretation, and visualization of biochemical interactions as they occur — translating photonic interference signals into dynamic molecular information.

This work extends the continuum of research developed across the author’s previous publications (from the 19ᵗʰ to the 26ᵗʰ), which progressively established the AI–Photonics framework as a unified scientific paradigm. Earlier studies explored photonic computation, energy–information coupling, and AI-optimized docking; this current paper represents their synthesis into a functional real-time molecular monitoring system.

By merging light-based quantum sensing with deep neural inference, AI-PIM reveals how photon interference encodes the subtleties of binding affinities, conformational transitions, and energy–information exchanges between biomolecules. This allows for molecular visualization not as static configurations, but as living processes evolving in time and energy space.

The model thus transforms molecular bioinformatics into an active, adaptive, and photonic discipline — capable of observing biochemical causality at the speed of light. This research inaugurates a new scientific direction termed Dynamic Quantum Bioinformatics, establishing the foundation for next-generation diagnostics, drug discovery, and bioenergetic computation.

Keywords: photonics, artificial intelligence, molecular interaction, quantum biology, real-time bioinformatics, photon interference, biocomputation, energy-information dynamics, AI-photon coupling, dynamic molecular visualization