Quantum Simulation of Preferred Tautomeric State Prediction

Shee, Yu; Yeh, Tzu‐Lan; Hsiao, Jen-Yueh; Yang, Ann Shawing; Lin, Yen‐Chu; Hsieh, Min-Hsiu

doi:10.48550/arxiv.2210.02977

Cited by 2 publications

(2 citation statements)

References 59 publications

(72 reference statements)

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“…The applications of quantum computing can be found in different fields, such as solving routing problems, 35,36 stock price forecasting, 37,38 multitask classification, 39 the two-player zero-sum game, 40 high-resolution handwritten digits gener-ation, 41 the discovery of molecular properties, 42−49 tautomeric state prediction, 50 and drug design. 51,52 We can obtain more efficient and accurate results by utilizing the technique of quantum annealer, quantum machine learning, and quantum algorithm.…”

Section: ■ Introductionmentioning

confidence: 99%

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

Kao¹,

Yang²,

Chiang

et al. 2023

J. Chem. Inf. Model.

Self Cite

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De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time- and resource-consuming, and it has a low probability of success. Recent advances in machine learning and deep learning technology have reduced the time and cost of the discovery process and therefore, improved pharmaceutical research and development. In this paper, we explore the combination of two rapidly developing fields with lead candidate discovery in the drug development process. First, artificial intelligence has already been demonstrated to successfully accelerate conventional drug design approaches. Second, quantum computing has demonstrated promising potential in different applications, such as quantum chemistry, combinatorial optimizations, and machine learning. This article explores hybrid quantum-classical generative adversarial networks (GAN) for small molecule discovery. We substituted each element of GAN with a variational quantum circuit (VQC) and demonstrated the quantum advantages in the small drug discovery. Utilizing a VQC in the noise generator of a GAN to generate small molecules achieves better physicochemical properties and performance in the goal-directed benchmark than the classical counterpart. Moreover, we demonstrate the potential of a VQC with only tens of learnable parameters in the generator of GAN to generate small molecules. We also demonstrate the quantum advantage of a VQC in the discriminator of GAN. In this hybrid model, the number of learnable parameters is significantly less than the classical ones, and it can still generate valid molecules. The hybrid model with only tens of training parameters in the quantum discriminator outperforms the MLP-based one in terms of both generated molecule properties and the achieved KL divergence. However, the hybrid quantum-classical GANs still face challenges in generating unique and valid molecules compared to their classical counterparts.

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Section: ■ Introductionmentioning

confidence: 99%

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

Kao¹,

Yang²,

Chiang

et al. 2023

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The applications of quantum computing can be found in difference fields, such as solving routing problem [35,36], the stock price forecasting [37,38], molecule properties discovery [39][40][41], tautomeric state prediction [42] and drug design [43]. We can obtain more efficient and accurate results by utilizing the technique of quantum annealer, quantum machine learning, and quantum algorithm.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

Kao¹,

Yang²,

Chiang³

et al. 2022

Preprint

View full text Add to dashboard Cite

The drug development process is not only time and resource-consuming but also has a low probability of success. Recent advances in machine learning and deep learning technology have reduced costs and improved pharmaceutical research and development. De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. It is also an important step to keep the drug discovery pipeline moving forward. Artificial intelligence has pushed the limit of conventional drug design approaches, and quantum computing has demonstrated its advantages in different applications, e.g., solving routing problems and stock price forecasting. We proposed a hybrid quantum-classical generative adversarial network (GAN) for small molecule discovery. We substituted each element of GAN with a variational quantum circuit (VQC) and demonstrated the quantum advantages in the small drug discovery. Utilizing a VQC in the noise generator of GAN to generate small molecules achieves better physicochemical properties and performance in the goal-directed benchmark than the classical counterpart. Moreover, we demonstrate the potential of a VQC with only tens of learnable parameters in the generator of GAN to generate small molecules, which is a more complex problem than other quantum computing applications. In the end, we also demonstrate the quantum advantage of a VQC in the discriminator of GAN. In this hybrid model, the number of learnable parameters is significantly less than the classical ones, and it can still generate valid molecules. The hybrid model with only tens of training parameters in the quantum discriminator outperforms the MLP-based one in terms of generated molecule properties and KL-divergence.

show abstract

Quantum Simulation of Preferred Tautomeric State Prediction

Cited by 2 publications

References 59 publications

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

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