AI-Driven Light-Spectrum Optimization for Photonic Drug Discovery
Year: 2025
Type: article
Abstract: This 22nd article in the PAI-DDP series presents the AI-Spectral Photonic Optimization Module (AIS-POM), an advanced extension of the Photonically-Assisted AI Drug Design Pipeline. The framework introduces adaptive spectral control, allowing AI algorithms to dynamically adjust photon wavelengths during simulations for optimal molecular interactions. Key features include: 1. Adaptive Spectral Photonics: Dynamic wavelength modulation to maximize energy transfer and binding efficiency. 2. AI-Based Spectral Control: Neural networks analyze simulation outputs in real time, refining light parameters for best molecular response. 3. Molecular Feedback Loop: Continuous adjustment of molecular models based on photonic feedback, enabling self-optimizing drug candidate generation. Applications span oncology, virology, neurodegenerative diseases, and antimicrobial drug design, providing high-speed, precise, and intelligent drug discovery. This framework represents a major leap in computational pharmacology, transforming light into an active, intelligent parameter driving molecular design.
Author: Ndenga, Barack
Institution: IndependenceFirst
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7091326337
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