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Letters in Drug Design & Discovery

Insight into Quantum Computing and Deep Learning Approach for Drug Design

Deepika Paliwal, Siva Naga Koteswara Rao Gudhanti, Devdhar Yadav, Prince Raj

Published Apr 27, 2023• 6 citations
Publisher
DOI
10.2174/1570180820666230427151812

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Quantum computing and deep learning are increasingly becoming an emerging paradigm that can transform the way complex computational problems are approached. This chapter offers an overview of Quantum Neural Networks and Deep Learning, presenting material on how quantum mechanics can be merged with traditional neural networks. Quantum computing principles, including those of superposition, entanglement, and quantum parallelism, offer a very promising toolkit to overcome some of the big issues being faced today--such as high-dimensional data processing, faster optimization, and reduced computation complexity. The chapter describes the architecture and operation of QNNs, compares them with classical deep learning models, and points out their applications in drug-discovery-based research, image recognition, and cryptography. This chapter also considers existing barriers, such as hardware limitations and noise in quantum systems, and sheds some light on the ongoing research efforts and future avenues in hybrid quantum-classical models. Additionally, the chapter gave a detailed insight into bridge quantum theory and artificial intelligence and provided a simple yet in-depth foundation for anyone interested in the quantum augmentation of deep learning's transformative possibilities.
Q-CureMatch is a quantum-assisted drug discovery platform that is used to quickly identify potential drugs. These drugs can be found using quantum algorithms in terms of their binding to a protein, which reduces the time needed for molecular docking experiments. In addition, deep learning helps in improving the accuracy given by statistical models, with the use of elements in personalized medicine, which helps to identify treatment suitable for each individual using their own genomic information. Also, using molecule structure , protein folding information, and response to drugs, itimproves the accuracy of the results. drug discovery, where an understanding of simulatedmolecules at the quantum level is needed . Even though the development of quantum computing machines is at a primitive level, hybrid methods using quantum-classical computing are becoming increasingly higher. The goal of this review paper is to examine current techniques using AI for drug discovery, investigate the latest research in quantum computing applied to molecular modeling, and review hybrid approaches that bridge both technologies. It also focuses on current drawbacks and future enhancements, by laying the ground for the design and implementation of the Q-CureMatch platform. Key Words: Machine learning, quantum computing, molecular docking, drug-discovery, compound screening
MOTIVATION: While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. RESULTS: We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug-design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the coronavirus (SARS-CoV-2) protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD. AVAILABILITY AND IMPLEMENTATION: Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.