What is the current state of research on using quantum computing for drug discovery and development?
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The current state of research on using quantum computing for drug discovery and development is rapidly advancing. Quantum computing hardware and software have made significant progress in recent years, leading to the emergence of clinical and medical proof-of-concept applications. These applications cover various areas such as genomics, clinical research, diagnostics, and treatments. Quantum machine learning (QML) algorithms, including quantum support vector classifiers and quantum neural networks, have shown promising results in generating new drug candidates, medical image classification, predicting patient persistence, forecasting treatment effectiveness, and tailoring radiotherapy. The combination of quantum computing and deep learning models has also been explored, enabling the explanation of ligand characteristics and interactions with biological targets, ultimately optimizing drug design. The integration of quantum computing and machine learning techniques is expected to revolutionize the drug discovery process, reducing time and resources needed for medicine development .
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| Papers (5) | Insight |
|---|---|
22 Jan 2023 | The paper mentions that quantum machine learning algorithms have been used in medical research for generating new molecular entities as drug candidates. |
05 Jan 2023 | The paper discusses the use of quantum computing in drug design and presents the results of encoding quantum data to a quantum genetic algorithm for predicting the best drug structure. However, it does not provide information on the current state of research in this field. |
| The paper mentions that quantum computing has been used in generating new molecular entities as drug candidates, indicating that there is research on using quantum computing for drug discovery and development. | |
25 Nov 2022 | The current state of research on using quantum computing for drug discovery and development is discussed in the paper. |
| The paper discusses the extensive use of quantum deep learning in drug design, including variational quantum Eigen solver, quantum convolutional deep neural networks, and quantized generative models. It suggests that quantum computing can significantly improve drug discovery and development. |
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