SIVANESH
S

Abstract: The confluence of quantum computing with artificial intelligence (AI) represents a paradigm change in computational drug discovery and precision medicine. This work investigates a hybrid approach that integrates Quantum Enhanced Machine Learning (QEML) with Transformer based Large Language Models (LLMs) to bypass the computational issues that are typically faced by conventional approaches to molecular modelling, drug target prediction, and patient stratification. The curse of dimensionality, non-linear feature spaces, and exponential cost of simulating molecular interactions afflict traditional ML pipelines.

Abstract: The architecture is built on a Modular Multi-Agent Ensemble, mirroring the "holistic view" of infrastructure described in the publication.

• Classifier Agent: Maps natural language project descriptions to the 21 CODATA activity categories (A–U), ensuring that "invisible" costs like N. Housing or R. Funding Acquisition are accounted for.
• Funding Expert Agent: Utilizes a logic-driven matrix to cross-reference identified activities against nine financing sources (e.g., Project Grants, User Fees, Institutional Funding).
• Compliance & Risk Agent: Performs a "stress test" on the proposed model, identifying economic paradoxes like the Prisoner's Dilemma or legal hurdles like EU Database Law.