Quantum AI for Drug Discovery and Material Science
DOI:
https://doi.org/10.63503/acset.978-81-995593-9-4.70Keywords:
Material Science, Drug Discovery, Molecular Property, Molecular Generation, Quantum ComputingAbstract
Materials science has historically depended on a blend of experimental methods and theoretical modelling to identify and create new materials with specific properties. Nevertheless, these processes may require considerable time and resources, and are frequently constrained by the intricacy of material systems. The rise of artificial intelligence (AI), particularly machine learning, has revolutionised materials science by offering powerful tools that accelerate the discovery, design, and characterisation of novel materials. This chapter emphasises the latest developments in AI applications in materials science for drug discovery. Artificial Intelligence is proficient at analysing intricate data, enhancing processes, and developing drug candidates, whereas quantum systems enable unparalleled molecular simulations, highly sensitive sensing, and accurate physical control. Applications in drug discovery are emphasised, encompassing molecular property prediction and molecular generation. This chapter focuses on technologies such as Nanomaterials, Biomaterials, Polymers, Metal-Organic Frameworks (MOFs), Hydrogels, and Smart Materials. This chapter emphasises the advantages of quantum technology in drug discovery: enhanced accuracy in molecular simulations, Accelerated drug screening, better comprehension of reactions, and Tailored medicine. Additionally, the challenges include: hardware limitations, the high cost of error correction, the maturity of algorithms, integration with classical methods, and issues related to cost and accessibility.
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[111] Garg, P., Bhatt, M., Parmar, R., & Arsalan, M. (2025). Generative AI: Evolution, Applications, Challenges, and Future Prospects. Applications, Challenges, and Future Prospects (May 17, 2025).
[112] Kumar, N., Kumar, Y., Khurana, D., Kumar, S., & Garg, P. (2025, November). A Hybrid Ensemble Learning Framework for Interpretable Student Performance Prediction Using Academic and Extracurricular Factors. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 666-672). IEEE.
[113] Khurana, D., Kumar, Y., Kumar, N., Kumar, S., & Garg, P. (2025, November). Transformer-Based Movie Recommendation System with Autoencoder-Enhanced Feature Compression. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 685-690). IEEE.
[114] Garg, P. (2025, November). Comparative Analysis of Various Neural Networks for Galaxy Classification. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 697-701). IEEE.
[115] Saggu, A. K., Babbar, N., & Garg, P. (2025, November). Health-Guard AI: Integrated Health Report Management and Analysis. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 614-623). IEEE.
[116] Kumar, S., Kumar, Y., Kumar, N., Khurana, D., & Garg, P. (2025, November). Hybrid FCM-DNN Model for Uncertainty-Aware Air Quality Classification Using Multi-Pollutant Data. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 679-684). IEEE.
[117] Babbar, N., Singh, H. V., Bendale, S., & Garg, P. (2025, November). Stock Market Price Prediction Using Big Data Analysis: A Performance Evaluation Study. In 2025, the 3rd International Conference on Computational Intelligence and Network Systems (CINS) (pp. 1-6). IEEE.
[118] Singh, A. K., Kori, G., Garg, P., & Srivastava, G. (2025, November). Bank Churn Prediction Using Machine Learning. In 2025, IEEE 7th International Conference on Computing, Communication and Automation (ICCCA) (pp. 1-6). IEEE.
[119] Bhardwaj, A., Das, A., Garg, P., & Yadav, S. (2026). Material-Driven Performance Analysis of a Vertical Nanowire Tunnel FET for Analogue Applications. Journal of Electronic Materials, 55(1), 1099-1110.
[120] Srivastava, A. K., Shankdhar, D., Ror, R., & Garg, P. (2026). Harnessing YOLOv5 for real-time object detection: A cloud-based approach. In Recent Advances in Computational Methods in Science and Technology (pp. 441-450). CRC Press.
[121] Srivastava, A. K., Shukla, A., Gupta, H., Saxena, K., & Garg, P. (2026). Towards an intelligent attendance management system with face recognition using the LBPH algorithm. In Recent Advances in Computational Methods in Science and Technology (pp. 8-15). CRC Press.
[122] Srivastava, A. K., Garg, P., & Pandey, H. (2026). Vedcure: Towards intelligent ayurvedic drug recommendation and disease prediction. In Recent Advances in Computational Methods in Science and Technology (pp. 16-23). CRC Press.
[123] Upadhyay, D., Garg, P., & Babbar, N. (2026). A blockchain- and IoT-based smart contract framework for efficient and secure product life management. Discover Internet of Things.
[124] Singh, A., Parmar, R., Bhardwaj, P., Sharma, V., & Garg, P. (2026). Fusion of Aerial Networks with Advanced Computing Paradigms. Edge Computing and Aerial Platforms, 355-367.
[125] Kumari, M., Baranwal, A., Sonal, & Garg, P. (2026). Application of Aerial Edge Computing in Disaster Management. Edge Computing and Aerial Platforms, 103-122.
[126] Aditi, Saraswat, P., Sharma, V., & Garg, P. (2026). Advances in Aerial Platforms and Edge Computing. Edge Computing and Aerial Platforms, 123-143.
[127] Garg, P., Arora, K., Bawane, R., Gupta, C., & Ahmed, K. (2025). Detection and Prevention of Cyber Attacks and Threats using AI.
[128] Ahmed, K., Ahmed, A., Khan, J., Garg, P., Seth, S., & Mallik, S. (2025). Principal Component Analysis-Based Clustering of Insecticides and Molecular Docking of Pyrethroid Insecticides.
[129] Kumar, B., Kumar, A., Nanwal, J., Garg, P., & Patnaik, P. (2025, November). Ensemble of YOLOv5 and Segment Anything Model for Brain Tumour Detection. In 2025, the 2nd International Conference on Advanced Computing and Emerging Technologies (ACET) (pp. 1-5). IEEE.
[130] Arsalan, M., Anas, M., & Garg, P. (2025). Transparent AI for Drug Discovery and Development. Available at SSRN 5844242.
[131] Singh, A., Bhardwaj, P., Garg, P., & Singh, N. (2026). Introduction to explainable artificial intelligence in healthcare. In Explainable AI in Clinical Practice (pp. 23-44). Academic Press.
[132] Kapoor, S., Singh, A., Garg, P., & Ramasamy, L. K. (2026). Explainable artificial intelligence in a diagnostic support system. In Explainable AI in Clinical Practice (pp. 131-145). Academic Press.
[133] Ahmed, K., Anas, M., & Garg, P. (2026). Case studies on unlocking the potential of Industry 4.0 for sustainable manufacturing through generative AI-driven innovations. Available at SSRN 6356958.
[134] Garg, P., & Oruganti, S. K. (2026, March). AI Assisted Routing Optimisation in Opportunistic IoT Networks using Machine Learning: A Comprehensive Review on Protocols & Simulators. In Sustainable Global Societies Initiative (Vol. 1, No. 4). Vibrasphere Technologies.
[135] Arsalan, M., Pokhrel, L., & Garg, P. (2026). Architecture, Components, and tools in Integrated AI-Augmented Intelligence: A design perspective. Components and tools in Integrated AI-Augmented Intelligence: A design perspective (March 19, 2026).
[136] Singh, H., Ahmed, K., & Garg, P. (2026). Human Versus Machine Customer Behaviour and Functional Differences. Available at SSRN 6441098.
[137] Saraswat, P., & Garg, P. (2026). Soft Computing In AI Agents.
[138] Saraswat, P., & Garg, P. (2026). Water Quality Prediction Using IOT Sensors and Deep Networks.
[139] Arsalan, M., Ahmed, K., & Garg, P. (2026). Machine learning for Anomaly detection in sensor networks. Available at SSRN 6441518.
[140] Kumari, M., & Garg, P. (2026). Hybrid Cloud Infrastructure: Models, Benefits, Security, and Challenges. Benefits, Security, and Challenges (March 19, 2026).
[141] Singh, H., & Garg, P. (2026). Demystifying Artificial Distributed Intelligence (ADI). Available at SSRN 6442698.
[142] Saraswat, P., & Garg, P. (2026). Human AI Collaboration: The Future of Clinical Decision Making.
[143] Saraswat, P., & Garg, P. (2026). Breaking Data Boundaries: Federated Learning in Digital Healthcare.
[144] Singh, A., Parmar, R., Bhardwaj, P., Sharma, V., & Garg, P. (2026). Fusion of Aerial Networks with Advanced Computing Paradigms. Edge Computing and Aerial Platforms, 355-367.
[145] Kumari, M., Baranwal, A., Sonal, & Garg, P. (2026). Application of Aerial Edge Computing in Disaster Management. Edge Computing and Aerial Platforms, 103-122.
[146] Aditi, Saraswat, P., Sharma, V., & Garg, P. (2026). Advances in Aerial Platforms and Edge Computing. Edge Computing and Aerial Platforms, 123-143.
[147] Garg, P. (2026). Zero-Trust Security Enforcement through AI-Powered Anomaly Detection in Cloud Systems. Journal of Artificial Intelligence in Governance and Public Policy (JAIGPP), 1(1), 1-8.
[148] Singh, A. P., Sharma, A., & Garg, P. (2026, January). AI-Powered Adaptive Mock Interview Generation System. In 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) (pp. 1421-1426). IEEE.
[149] Raghav, A., Mishra, A., & Garg, P. (2026, January). Enhancing Healthcare Access: An AI-driven Chatbot for Doctor Appointment Management. In 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) (pp. 910-914). IEEE.
[150] Kumar, B., Chauhan, D., Singh, H., Verma, H., Sahu, K., & Garg, P. (2026, January). Decoding Linear A with Artificial Intelligence: A Comprehensive Machine Learning and NLP Framework. In 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) (pp. 1-7). IEEE.
[151] Gupta, V., Chakravarti, L., Akhtar, M. M., Maheshwari, P., Garg, P., & Tiwari, D. (2026, January). A Sentence-Level Risk Estimator for Identifying Hallucinations in Generative AI. In 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) (pp. 1619-1626). IEEE.
[152] Patnaik, P., & Garg, P. (2026). Principles of Artificial Intelligence: Cognitive Architectures, Large Language Models, and Computational Limits. Deep Science Publishing.
[153] Singh, K., & Garg, P. (2026). Trustworthy Deep Learning: Robustness, Uncertainty Quantification, and Adversarial Resilience. Deep Science Publishing.
[154] Garg, P. (2026). Survey of Load Balancing Strategies in Fog-Cloud Architectures for IoT Integration. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(2), 595-604.
