International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 08 | Aug 2025
p-ISSN: 2395-0072
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Bogar: Quantum-Enhanced AI with Conversational Interface for Drug Discovery and Personalized Medicine Sivanesh S1 1Data Scientist, MTech of AI and ML, BITS PILANI, Rajasthan, INDIA
---------------------------------------------------------------------***--------------------------------------------------------------------challenges encompass the curse of dimensionality, lack of Abstract - The confluence of quantum computing with
correct simulation of molecular interactions, bad generalization across patient groups, and computational inefficiency for screening enormous chemical spaces. Expressive deep learning models require vast amounts of resources for training and tend to lack interpretability, hence being less practical in regulated and sensitive fields such as healthcare. Further, existing models are also incapable of integrating patient-specific information from multiomic data and scaling in an efficient manner with advancing biological complexity. The absence of explainable, real-time decision support systems is also another hindrance to translational use in clinical settings. In addition, generative molecule design models tend to neglect chemical validity and pharmacological relevance without intensive post-processing. Therefore, there exists a pressing need for an interpretable, scalable, and computationally effective AI framework that can combine biological data, speed up molecular discovery, and enable domain-specific conversational agents for precision healthcare applications.
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. Key Words: Quantum Machine Learning (QML), Drug Discovery, Personalized Medicine, Quantum Neural Networks (QNN), Large Language Models (LLMs), Hybrid Quantum Classical Models and Personalized Medicine.)
1.INTRODUCTION Computational bottlenecks involved in modelling intricate biomolecular interactions and high-dimensional clinical data often hinder conventional drug discovery and personalized medicine pipelines. Even though classical machine learning algorithms are powerful, they cannot cope with the nonlinearity and combinatorial complexity of molecular and patient-specific data, leading to poor predictive modelling and decision-making. New computational paradigms with both precision and scalability are needed with the explosion of large-scale biomedical data and increasing granularity of multiomics and chemical data. Processing data in exponentially richer spaces is enabled by the inherent capabilities of quantum computing, such as superposition, entanglement, and quantum parallelism. Recent developments in Quantum Machine Learning (QML) and hybrid AI architectures suggest that integrating quantum algorithms with deep learning models—especially transformer-based Large Language Models (LLMs)—has the potential to revolutionize precision medicine by enabling quicker, more accurate, and more interpretable AI-driven insights.
1.2 Objectives of the Research This work seeks to develop and apply a QuantumEnhanced AI Framework for computational drug discovery and personalized medicine by incorporating hybrid quantum-classical models with intelligent multi-agent systems. The following specific technical aims control the investigation. Develop a Hybrid QML Pipeline for Molecular Generation and Prediction and construct quantum-classical models hosted via Hugging Face Spaces. These models will be used to learn molecular representations, generate druglike candidates in SELFIES/SMILES formats, and evaluate them against bioactivity filters such as QED, Lipinski’s Rule, and binding affinity predictions.
2. DRUG DISCOVERY MODULE Bogar's "Quantum Classical Neuro-Architecture " drug discovery approach combines classical and quantum paradigms of computing. It starts with PreNetInput providing classical molecular information to a QuantumCircuit (QNode ), which loads qubits and performs quantum rotations to store molecular information. The resulting quantum state is routed through an AngelEmbelinger , where additional rotations and key entanglement operations may be undertaken to enhance
1.1 Problem Statement and Analysis Even with accelerated advancements in AI-based drug discovery and precision medicine, traditional machine learning systems have severe shortcomings when it comes to high-dimensional, nonlinear biomedical data. The
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