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HYBRID QUANTUM NEURAL NETWORKS WITH VARIATIONAL QUANTUM REGRESSOR FOR ENHANCING QSPR MODELING OF CO2-CAPTURING AMINE

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arXiv 2025年

作者： Cho, Hyein Kim, Jeonghoon Lim, Hocheol The Interdisciplinary Graduate Program in Integrative Biotechnology & Translational Medicine Yonsei University Incheon Korea Republic of Incheon Korea Republic of

Accurate amine property prediction is essential for optimizing CO2 capture efficiency in postcombustion processes. Quantum machine learning (QML) can enhance predictive modeling by leveraging superposition, entanglement, and interference to capture complex correlations. In this study, we developed hybrid quantum neural networks (HQNN) to improve quantitative structure-property relationship modeling for CO2-capturing amines. By integrating variational quantum regressors with classical multi-layer perceptrons and graph neural networks, quantum advantages were explored in physicochemical property prediction under noiseless conditions and robustness was evaluated against quantum hardware noise using IBM quantum systems. Our results showed that HQNNs improve predictive accuracy for key solvent properties, including basicity, viscosity, boiling point, melting point, and vapor pressure. The fine-tuned and frozen pre-trained HQNN models with 9 qubits consistently achieved the highest rankings, highlighting the benefits of integrating quantum layers with pretrained classical models. Furthermore, simulations under hardware noise confirmed the robustness of HQNNs, maintaining predictive performance. Overall, these findings emphasize the potential of hybrid quantum-classical architectures in molecular modeling. As quantum hardware and QML algorithms continue to advance, practical quantum benefits in QSPR modeling and materials discovery are expected to become increasingly attainable, driven by improvements in quantum circuit design, noise mitigation, and scalable architectures. © 2025, CC BY-NC-ND.

关键词： Quantum entanglement

SAGE-Amine: Generative Amine Design with Multi-Property Optimization for Efficient CO2 Capture

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arXiv 2025年

作者： Lim, Hocheol Cho, Hyein Kim, Jeonghoon Incheon21983 Korea Republic of The Interdisciplinary Graduate Program in Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of

Efficient CO2 capture is vital for mitigating climate change, with amine-based solvents being widely used due to their strong reactivity with CO2. However, optimizing key properties such as basicity, viscosity, and absorption capacity remains challenging, as traditional methods rely on labor-intensive experimentation and predefined chemical databases, limiting the exploration of novel solutions. Here, SAGE-Amine was introduced, a generative modeling approach that integrates Scoring-Assisted Generative Exploration (SAGE) with quantitative structure-property relationship models to design new amines tailored for CO2 capture. Unlike conventional virtual screening restricted to existing compounds, SAGE-Amine generates novel amines by leveraging autoregressive natural language processing models trained on amine datasets. SAGE-Amine identified known amines for CO2 capture from scratch and successfully performed single-property optimization, increasing basicity or reducing viscosity or vapor pressure. Furthermore, it facilitated multi-property optimization, simultaneously achieving high basicity with low viscosity and vapor pressure. The 10 top-ranked amines were suggested using SAGE-Amine and their thermodynamic properties were further assessed using COSMO-RS simulations, confirming their potential for CO2 capture. These results highlight the potential of generative modeling in accelerating the discovery of amine solvents and expanding the possibilities for industrial CO2 capture applications. © 2025, CC BY-NC-ND.

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SAGE-Amine: Generative Amine design with multi-property optimization for efficient CO 2 capture

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Carbon Capture Science & Technology

Carbon Capture Science & Technology 2025年 16卷

作者： Hocheol Lim Hyein Cho Jeonghoon Kim Kyoung Tai No Bioinformatics and Molecular Design Research Center (BMDRC) Incheon 21983 Republic of Korea The Interdisciplinary Graduate Program in Integrative Biotechnology & Translational Medicine Yonsei University Incheon 21983 Republic of Korea

Efficient CO 2 capture is vital for mitigating climate change, with amine-based solvents being widely used due to their strong reactivity with CO 2 . However, optimizing key properties such as basicity, viscosity, and absorption capacity remains challenging, as traditional methods rely on labor-intensive experimentation and predefined chemical databases, limiting the exploration of novel solutions. Here, SAGE-Amine was introduced, a generative modeling approach that integrates Scoring-Assisted Generative Exploration (SAGE) with quantitative structure-property relationship models to design new amines tailored for CO 2 capture. Unlike conventional virtual screening restricted to existing compounds, SAGE-Amine generates novel amines by leveraging autoregressive natural language processing models trained on amine datasets. SAGE-Amine identified known amines for CO 2 capture from scratch and successfully performed single-property optimization, increasing basicity or reducing viscosity or vapor pressure. Furthermore, it facilitated multi-property optimization, simultaneously achieving high basicity with low viscosity and vapor pressure. The 10 top-ranked amines were suggested using SAGE-Amine and their thermodynamic properties were further assessed using COSMO-RS simulations, confirming their potential for CO 2 capture. These results highlight the potential of generative modeling in accelerating the discovery of amine solvents and expanding the possibilities for industrial CO 2 capture applications.

关键词： Material discovery De novo molecular design Quantitative structure-property relationship Solvent design Amine design

Characterization of Antioxidative Flavonoids in Wild and Cultivated Soybean by Ion-Filtering Strategy-Combined Lc-Hrmsn Analysis

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SSRN

SSRN 2024年

作者： No, Kyoung Tai Kim, Kwan Joong Cho, Min Hyung Lee, Sangwon Ha, Jang-Ho Chu, Sung-June Chung, Gyuhwa Kim, Dae-Ok Bioinformatics & Molecular Design Research Center Incheon21983 Korea Republic of Department of Biotechnology Chonnam National University Yeosu59626 Korea Republic of Graduate School of Biotechnology Kyung Hee University Yongin17104 Korea Republic of Interdisciplinary Graduate Program Department of Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of Yonsei University Korea Republic of

To profiling flavonoids in wild soybean (WB;Glycine soja Sieb & Zucc.) and cultivated soybean (CB;Glycine max (L.) Merr.), liquid chromatography-high resolution mass spectrometry (LC-HRMS), antioxidant assays, and chemometrics method were used. Total 70 flavonoids were tentatively identified with ion filter strategies containing precursor ion list, mass defect filter, nitrogen rule filter, and diagnostic ion filter from 20,999 features in WB and CB. Semi-quantification result revealed that WB has 1.4-, 93.1-, 6.3-, and 1.9-fold higher contents in isoflavone, flavan, procyanidin, and flavone than that of CB. In antioxidant assays, WB showed 1.89- to 5.83-fold higher antioxidant capacities than CB in four antioxidant capacity assays. In orthogonal partial least squares discriminant analysis, WB and CB were clearly classified, and 31 characteristic flavonoids were identified by variable importance in projection. Furthermore, epicatechin and procyanidins of WB showed significantly positive correlation with antioxidant capacities by Pearson's correlation. © 2024, The Authors. All rights reserved.

关键词： Flavonoids

Quantum computational study of chloride ion attack on chloromethane for chemical accuracy and quantum noise effects with UCCSD and k-UpCCGSD ansatzes

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arXiv 2021年

作者： Lim, Hocheol Jeon, Hyeon-Nae Rhee, June-Koo Oh, Byungdu No, Kyoung Tai The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine Yonsei University Incheon Korea Republic of Incheon Korea Republic of Department of Biotechnology Yonsei University Seoul Korea Republic of QuNova Computing Inc. Daejeon Korea Republic of Baobab AiBIO Co. Ltd. Incheon Korea Republic of SKKU Advanced Institute of Nanotechnology Sungkyunkwan University Suwon Korea Republic of

Quantum computing is expected to play an important role in solving the problem of huge computational costs in various applications by utilizing the collective properties of quantum states, including superposition, interference, and entanglement, to perform computations. Quantum mechanical (QM) methods are candidates for various applications and can provide accurate absolute energy calculations in structure-based methods. QM methods are powerful tools for describing reaction pathways and their potential energy surfaces (PESs). In this study, we applied quantum computing to describe the PES of the bimolecular nucleophilic substitution (SN2) reaction between chloromethane and chloride ions. We performed noiseless and noise simulations using quantum algorithms and compared the accuracy and noise effects of the ansatzes. In noiseless simulations, the results from UCCSD and k-UpCCGSD are similar to those of full configurational interaction (FCI) with the same active space, which indicates that quantum algorithms can describe the PES of the SN2 reaction. In noise simulations, UCCSD is more susceptible to quantum noise than k-UpCCGSD. Therefore, k-UpCCGSD can serve as an alternative to UCCSD to reduce quantum noisy effects in the noisy intermediate-scale quantum era, and k-UpCCGSD is sufficient to describe the PES of the SN2 reaction in this work. The results showed the applicability of quantum computing to the SN2 reaction pathway and provided valuable information for structure-based molecular simulations with quantum computing. © 2021, CC BY-NC-ND.

关键词： Quantum chemistry

Cultivation of Chlorella sp. HS2 using wastewater from soy sauce factory

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Chemosphere 2023年 342卷 140162页

作者： Kim, Minsik Lee, Sang-Ah Yun, Jin-Ho Ko, So-Ra Cho, Dae-Hyun Kim, Hee Su Oh, Hee-Mock Kim, Hee-Sik Ahn, Chi-Yong Daejeon34141 Korea Republic of Dept. of Biological Engineering Inha University Incheon22212 Korea Republic of Faculty of Biotechnology College of Applied Life Sciences Jeju National University Jeju63243 Korea Republic of Interdisciplinary Graduate Program in Advance Convergence Technology and Science Jeju National University Jeju63243 Korea Republic of Department of Integrative Biotechnology Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of FarmHannong Chungcheongnam-do Nonsan33010 Korea Republic of

Incorporation of wastewater from industrial sectors into the design of microalgal biorefineries has significant potential for advancing the practical application of this emerging industry. This study tested various food industrial wastewaters to assess their suitability for microalgal cultivation. Among these wastewaters, defective soy sauce (DSS) and soy sauce wastewater (SWW) were chosen but DSS exhibited the highest nutrient content with 13,500 ppm total nitrogen and 3051 ppm total phosphorus. After diluting DSS by a factor of 50, small-scale cultivation of microalgae was conducted to optimize culture conditions. SWW exhibited optimal growth at 25–30 °C and 300–500 μE m−2 s−1, while DSS showed optimal growth at 30–35 °C. Based on a 100-mL lab-scale and 3-L outdoor cultivation with an extended cultivation period, DSS outperformed SWW, exhibiting higher final biomass productivity. Additionally, nutrient-concentrated nature of DSS is advantageous for transportation at an industrial scale, leading us to select it as the most promising feedstock for microalgal cultivation. With further optimization, DSS has the potential to serve as an effective microalgal cultivation feedstock for large-scale biomass production. © 2023 The Authors

关键词： Microalgae

Structure-Based Optimization of Tead Inhibitors: Exploring a Novel Subpocket Near Glu347 for the Treatment of Nf2-Mutant Cancer

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SSRN

SSRN 2024年

作者： Kim, Jin Kwan Kim, Jinhyuk Kim, Hadong Jin, Haiyan Yoo, Youngki Fei, Xiang Maeng, Han-Joo Seo, Seung-Yong Han, Gyoonhee No, Kyoung Tai Baobab AiBIO Co. Ltd. Incheon21983 Korea Republic of Gachon Institute of Pharmaceutical Sciences College of Pharmacy Gachon University Incheon21936 Korea Republic of Department of Integrative Biotechnology Yonsei University Incheon21983 Korea Republic of Incheon21983 Korea Republic of Department of Pharmacy College of Pharmacy Yonsei University Incheon21983 Korea Republic of Institute of Convergence Science and Technology Yonsei University Incheon21983 Korea Republic of Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine Korea Republic of

The Hippo pathway is a signaling pathway implicated in various diseases, including cancer, that regulates the levels of yes-associated protein (YAP) and PDZ-binding motif (TAZ) proteins. These proteins bind to transcriptional enhanced associate domain (TEAD) proteins and function as transcription factors in the nucleus, producing oncogenic target genes such as CTGF and CYR61. TEAD proteins require palmitoylation via a covalent bond with cysteine in the central pocket to bind YAP/TAZ. Therefore, competitive inhibition that prevents palmitoylation could serve as an effective anticancer strategy. In this study, we analyzed the crystal structures of known inhibitors to identify new binding spots that were previously unexplored, with the aim of discovering more potent compounds using structure-based drug design. Consequently, we identified a novel hydrogen-bonding site and discovered Compound 2, which effectively binds to this site, as confirmed by X-ray crystallography. Furthermore, Compound 2 exhibited stable pharmacokinetic properties and demonstrated impressive efficacy in a mouse xenograft model. © 2024, The Authors. All rights reserved.

关键词： Transcription

Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model

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arXiv 2024年

作者： Zhou, Peng Wang, Jianmin Li, Chunyan Wang, Zixu Liu, Yiping Sun, Siqi Lin, Jianxin Wei, Leyi Cai, Xibao Lai, Houtim Liu, Wei Wang, Longyue Liu, Yuansheng Zeng, Xiangxiang College of Information Science and Engineering Hunan University Changsha China The Interdisciplinary Graduate Program in Integrative Biotechnology Yonsei University Incheon21983 Korea Republic of School of Informatics Yunnan Normal University Kunming650500 China Department of Computer Science University of Tsukuba Tsukuba3058577 Japan Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Shanghai AI Laboratory Shanghai200232 China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University China School of Informatics Xiamen University Xiamen China Tencent AI Lab China

While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint molecular generation large language model, TSMMG, which, akin to a student, incorporates knowledge from various small models and tools, namely, the 'teachers'. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers', enabling it to generate novel molecules that conform to the descriptions through various text prompts. We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements across two-, three-, and four-constraint tasks, with an average molecular validity of over 99% and success ratio of 82.58%, 68.03%, and 67.48%, respectively. The model also exhibits adaptability through zero-shot testing, creating molecules that satisfy combinations of properties that have not been encountered. It can comprehend text inputs with various language styles, extending beyond the confines of outlined prompts, as confirmed through empirical validation. Additionally, the knowledge distillation feature of TSMMG contributes to the continuous enhancement of small models, while the innovative approach to dataset construction effectively addresses the issues of data scarcity and quality, which positions TSMMG as a promising tool in the domains of drug discovery and materials science. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

World Congress integrative Medicine & Health 2017: Part one: Berlin, Germany. 3-5 May 2017

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BMC Complement Altern Med 2017年第SUPPL 1期17卷 322-322页

作者： Benno Brinkhaus Torkel Falkenberg Aviad Haramati Stefan N. Willich Josephine P. Briggs Merlin Willcox Klaus Linde Töres Theorell Lisa M. Wong Jeffrey Dusek Darong Wu David Eisenberg Bettina Berger Kathi Kemper Beate Stock-Schröer Hedda Sützl-Klein Rosaria Ferreri Gary Kaplan Harald Matthes Gabriele Rotter Elad Schiff Zahi Arnon Eckhard Hahn Christina M. Luberto David Martin Silke Schwarz Diethard Tauschel Andrew Flower Harsha Gramminger Hedwig H. Gupta S. N. Gupta Annette Kerckhoff Christian S. Kessler Andreas Michalsen Eun S. Kim Eun H. Jang Rana Kim Sae B. Jan Martin Mittwede Wiebke Mohme Eran Ben-Arye Massimo Bonucci Bashar Saad Thomas Breitkreuz Elio Rossi Rejin Kebudi Michel Daher Samaher Razaq Nahla Gafer Omar Nimri Mohamed Hablas Gunver Sophia Kienle Noah Samuels Michael Silbermann Lena Bandelin Anna-Lena Lang Eva Wartner Christoph Holtermann Maxwell Binstock Robert Riebau Edin Mujkanovic Holger Cramer Romy Lauche Andres Michalsen Lesley Ward Dominik Irnich Wolfram Stör Geoffrey Burnstock Hans-Georg Schaible Thomas Ots Jost Langhorst Tobias Sundberg Catherina Amarell Melanie Anheyer Marion Eckert Mercedes Ogal Annette Schönauer Birgit Reisenberger Bernhard Brand Dennis Anheyer Gustav Dobos Matthias Kroez Aldo Ammendola Jun J. Mao Claudia Witt Yufei Yang Miriam Oritz Markus Horneber Petra Voiß Alexandra von Rosenstiel Catharina Amarell Friedemann Schad Marc Schläppi Matthias Kröz Arndt Büssing Gil Bar-Sela David Avshalomov Samuel Attias Sian Cotton Miek Jong Mats Jong Christian Scheffer Friedrich Edelhäuser Abdullah AlBedah Myeong Soo Lee Mohamed Khalil Keiko Ogawa Yoshiharu Motoo Junsuke Arimitsu Masao Ogawa Genki Shimizu Rainer Stange Karin Kraft Kenny Kuchta Kenji Watanabe D Bonin Harald Gruber Sabine Koch Urs Pohlmann Christine Caldwell Barbara Krantz Ria Kortum Lily Martin Lisa S. Wieland Ben Kligler Susan Gould-Fogerite Yuqing Zhang John J. Riva Michael Lumpkin Emily Ratner Liu Ping Pei Jian Gesa-Meyer Hamme Xiaosong Mao Han Chouping Sven Schröder Josef Hummelsberger Michael Wullinger Marc Brodzky Chr Institute for Social Medicine Epidemiology and Health Economics Charité – Universitätsmedizin Berlin Berlin Germany Department of Neurobiology Care Sciences and Society Division of Nursing Research Group Integrative Care Karolinska Institutet Stockholm Sweden I C – The Integrative Care Science Center Järna Sweden Department of Biochemistry Molecular and Cellular Biology Georgetown University Medical Center Washington DC USA Department of Medicine Georgetown University Medical Center Washington DC USA NCCIH NIH Bethesda MD USA Department of Primary Care and Population Sciences University of Southampton Aldermoor Health Centre Coxford Rd Southampton 2016 5ST UK Institute of General Practice Technical University Munich Munich Germany Department of Neuroscience Karolinska Institute Stockholm Sweden Stress Research Institute Stockholm University Stockholm Sweden Arts and Humanities Initiative Harvard Medical School Boston MA 02115 USA Psychosomatic medicine Neuropsychology Allina Health Minneapolis MN USA nd Affiliated Hospital of Guangzhou University of Chinese Medicine Guangzhou China Department of Nutrition Harvard University T H Chan School of Public Health Boston MA 02115 USA Department of Health University of Witten/Herdecke Witten/Herdecke Germany OSU Blacklick OH USA Carstens-Foundation and FORUM D-45276 Essen Germany ESIHR (European Society for Integrative Health Care) A-1070 Vienna Austria Hospital Centre of Integrated Medicine Hospital of Pitiglian o ASL SudEst Toscana Grosseto Italy The Kaplan Center for Integrative Medicine Georgetown University McLean VA USA Hospital Havelhöhe Berlin Germany Institute for Social Medicine Epidemiology and Health Economics Charité University Hospital Berlin Germany Internal medicine and Complementary medicine service Bnai Zion Medical Center Haifa Israel Complementary-Integrative Surgery Service Bnai Zion Medical Center Haifa Israel The Emek Yezreel Academic College Yezreel Valley Israel Medicine