A Quantum Finite Automata Approach to Modeling the Chemical Reactions

Bhatia, Amandeep Singh; Zheng, Shenggen

doi:10.3389/fphy.2020.547370

Cited by 3 publications

(2 citation statements)

References 51 publications

(62 reference statements)

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“…Later, Liu et al incorporated hybrid quantum generative adversarial networks (QGANs) to learn the complex patterns in molecules, quantum convolutional neural networks (QCNNs) for classification of protein packets, and quantum variational autoencoders (QVAE) to generate small molecules. Bhatia and Zheng modeled several chemical reactions using theoretical quantum computational models.…”

Section: Prior Workmentioning

confidence: 99%

Quantum Machine Learning Predicting ADME-Tox Properties in Drug Discovery

Bhatia,

Saggi,

Kais

2023

J. Chem. Inf. Model.

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In the drug discovery paradigm, the evaluation of absorption, distribution, metabolism, and excretion (ADME) and toxicity properties of new chemical entities is one of the most critical issues, which is a time-consuming process, immensely expensive, and poses formidable challenges in pharmaceutical R&D. In recent years, emerging technologies like artificial intelligence (AI), big data, and cloud technologies have garnered great attention to predict the ADME and toxicity of molecules. Currently, the blend of quantum computation and machine learning has attracted considerable attention in almost every field ranging from chemistry to biomedicine and several engineering disciplines as well. Quantum computers have the potential to bring advances in high-throughput experimental techniques and in screening billions of molecules by reducing development costs and time associated with the drug discovery process. Motivated by the efficiency of quantum kernel methods, we proposed a quantum machine learning (QML) framework consisting of a classical support vector classifier algorithm with a kernel-based quantum classifier. To demonstrate the feasibility of the proposed QML framework, the simplified molecular input line entry system (SMILES) notation-based string kernel, combined with a quantum support vector classifier, is used for the evaluation of chemical/drug ADME-Tox properties. The proposed quantum machine learning framework is validated and assessed via large-scale simulations. Based on our results from numerical simulations, the quantum model achieved the best performance as compared to classical counterparts in terms of the area under the curve of the receiver operating characteristic curve (AUC ROC; 0.80–0.95) for predicting outcomes on ADME-Tox data sets for small molecules, with a different number of features. The deployment of the proposed framework in the pharmaceutical industry would be extremely valuable in making the best decisions possible.

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Section: Prior Workmentioning

confidence: 99%

Quantum Machine Learning Predicting ADME-Tox Properties in Drug Discovery

Bhatia,

Saggi,

Kais

2023

J. Chem. Inf. Model.

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“…Quantum computing is becoming a very hot research topic nowadays. There are various computing models [1,2], including quantum query complexity [3][4][5], communication complexity [6,7], and quantum finite automata [8][9][10][11] [12,13]. Game theory is an important branch of modern mathematics, which has been applied in many fields, such as economics and business, project management, and computer science.…”

Section: Introductionmentioning

confidence: 99%

A linear algorithm for the restricted subtraction games

Yang

He²,

Li³

et al. 2022

Front. Phys.

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The subtraction game is a well-known problem in the field of game theory, which is often called a one-heap Nim game. There are two players, a heap of tokens, and a strategy matrix, in this game. It is called a restricted subtraction game if some constraints are imposed on the strategy matrix. The subtraction game could be solved by a classical algorithm with a query complexity no less than O(N2) and computed by an O(N32logN) quantum query algorithm with an error probability ϵ≤1N. The restricted subtraction game proposed by Huang et al. can be computed by an exact quantum algorithm with O(N32) queries. They also proved that the classical exact query complexity is Θ(N2). In this study, we use an array and an adjacency list to replace the strategy matrix and propose another more general subtraction game than the one introduced by Huang et al. Based on dynamic programming and the replaced game strategy, two linear classical query algorithms are designed to solve the restricted subtraction game proposed by Huang and this study, respectively.

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