A Hybrid Quantum Computing Pipeline for Real World Drug Discovery
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910539404279808 |
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| author | Li, Weitang Yin, Zhi Li, Xiaoran Ma, Dongqiang Yi, Shuang Zhang, Zhenxing Zou, Chenji Bu, Kunliang Dai, Maochun Yue, Jie Chen, Yuzong Zhang, Xiaojin Zhang, Shengyu |
| author_facet | Li, Weitang Yin, Zhi Li, Xiaoran Ma, Dongqiang Yi, Shuang Zhang, Zhenxing Zou, Chenji Bu, Kunliang Dai, Maochun Yue, Jie Chen, Yuzong Zhang, Xiaojin Zhang, Shengyu |
| contents | Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing \rev{a hybrid} quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03759 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Hybrid Quantum Computing Pipeline for Real World Drug Discovery Li, Weitang Yin, Zhi Li, Xiaoran Ma, Dongqiang Yi, Shuang Zhang, Zhenxing Zou, Chenji Bu, Kunliang Dai, Maochun Yue, Jie Chen, Yuzong Zhang, Xiaojin Zhang, Shengyu Chemical Physics Biological Physics Quantum Physics Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing \rev{a hybrid} quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows. |
| title | A Hybrid Quantum Computing Pipeline for Real World Drug Discovery |
| topic | Chemical Physics Biological Physics Quantum Physics |
| url | https://arxiv.org/abs/2401.03759 |