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Liu C, Zou R, Mo F. Infrared Spectra Prediction for Functional Group Region Utilizing a Machine Learning Approach with Structural Neighboring Mechanism. Anal Chem 2024; 96:15550-15562. [PMID: 39298179 DOI: 10.1021/acs.analchem.4c01972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/02/2024]
Abstract
Infrared (IR) spectroscopy is a pivotal technique in chemical research for elucidating molecular structures and dynamics through vibrational and rotational transitions. However, the intricate molecular fingerprints characterized by unique vibrational and rotational patterns present substantial analytical challenges. Here, we present a machine learning approach employing a structural neighboring mechanism tailored to enhance the prediction and interpretation of infrared spectra. Our model distinguishes itself by honing in on chemical information proximal to functional groups, thereby significantly bolstering the accuracy, robustness, and interpretability of spectral predictions. This method not only demystifies the correlations between infrared spectral features and molecular structures but also offers a scalable and efficient paradigm for dissecting complex molecular interactions.
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Affiliation(s)
- Chengchun Liu
- School of Materials Science and Engineering, Peking University, Beijing 100871, China
| | - Ruqiang Zou
- School of Materials Science and Engineering, Peking University, Beijing 100871, China
- AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- School of Advanced Materials, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Fanyang Mo
- School of Materials Science and Engineering, Peking University, Beijing 100871, China
- AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- School of Advanced Materials, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- Guangdong Provincial Key Laboratory of Nano-Micro Materials Research, Peking University Shenzhen Graduate School, Shenzhen 518055, China
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2
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Chen B, Li H, Huang R, Tang Y, Li F. Deep learning prediction of electrospray ionization tandem mass spectra of chemically derived molecules. Nat Commun 2024; 15:8396. [PMID: 39333165 PMCID: PMC11436754 DOI: 10.1038/s41467-024-52805-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 09/16/2024] [Indexed: 09/29/2024] Open
Abstract
Chemical derivatization is a powerful strategy to enhance sensitivity and selectivity of liquid chromatography-mass spectrometry for non-targeted analysis of chemicals in complex mixtures. However, it remains impossible to obtain large sets of reference spectra for chemically derived molecules (CDMs), representing a major barrier in real-world applications. Herein, we describe a deep learning approach that enables accurate prediction of electrospray ionization tandem mass spectra for CDMs (DeepCDM). DeepCDM is established by transfer learning from a generic spectrum predicting model using a small set of experimentally acquired tandem mass spectra of CDMs, which converts a generic model with low predictability for CDMs into a specialized model with high predictability. We demonstrate DeepCDM by predicting electrospray ionization tandem mass spectra of dansylated molecules. The success in establishing Dns-MS further enables the development of DnsBank, a dansylation-specialized in silico spectral library. DnsBank achieves significant increases of accurate annotation rates of dansylated molecules, facilitating discovery of new hazardous pollutants from an environmental study of leather industrial wastewater. DeepCDM is also highly versatile for other classes of CDMs. Therefore, we envision that DeepCDM will pave a way for high-throughput identification of CDMs in non-targeted analysis to dig unknowns with potential health impacts from emerging anthropogenic chemicals.
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Affiliation(s)
- Bin Chen
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, China
| | - Hailiang Li
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, China
| | - Rongfu Huang
- Sichuan Provincial Key Laboratory of Universities on Environmental Science and Engineering, MOE Key Laboratory of Deep Earth Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu, Sichuan, 610064, China
| | - Yanan Tang
- Analytical & Testing Center, Sichuan University, Chengdu, Sichuan, 610064, China.
| | - Feng Li
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, China.
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3
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Zhu Y, Peng J, Xu C, Lan Z. Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation. J Phys Chem Lett 2024; 15:9601-9619. [PMID: 39270134 DOI: 10.1021/acs.jpclett.4c01751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest in recent years. However, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted to developing novel and easy-to-use analysis tools for the identification of photoinduced reaction channels and the comprehensive understanding of complicated molecular motions in NAMD simulations. Here, we tried to survey recent advances in this field, particularly to focus on how to use unsupervised ML methods to analyze the trajectory-based NAMD simulation results. Our purpose is to offer a comprehensive discussion on several essential components of this analysis protocol, including the selection of ML methods, the construction of molecular descriptors, the establishment of analytical frameworks, their advantages and limitations, and persistent challenges.
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Affiliation(s)
- Yifei Zhu
- MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Jiawei Peng
- MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Chao Xu
- MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhenggang Lan
- MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
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4
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Zhu Y, Li M, Xu C, Lan Z. Quantum Chemistry Dataset with Ground- and Excited-state Properties of 450 Kilo Molecules. Sci Data 2024; 11:948. [PMID: 39209851 PMCID: PMC11362161 DOI: 10.1038/s41597-024-03788-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Accepted: 08/15/2024] [Indexed: 09/04/2024] Open
Abstract
Due to rapid advancements in deep learning techniques, the demand for large-volume high-quality datasets grows significantly in chemical research. We developed a quantum-chemistry database that includes 443,106 small organic molecules with sizes up to 10 heavy atoms including C, N, O, and F. Ground-state geometry optimizations and frequency calculations of all compounds were performed at the B3LYP/6-31G* level with the BJD3 dispersion correction, while the excited-state single-point calculations were conducted at the ωB97X-D/6-31G* level. Totally twenty-seven molecular properties, such as geometric, thermodynamic, electronic and energetic properties, were gathered from these calculations. Meanwhile, we also established a comprehensive protocol for the construction of a high-volume quantum-chemistry dataset. Our QCDGE (Quantum Chemistry Dataset with Ground- and Excited-State Properties) dataset contains a substantial volume of data, exhibits high chemical diversity, and most importantly includes excited-state information. This dataset, along with its construction protocol, is expected to have a significant impact on the broad applications of machine learning studies across different fields of chemistry, especially in the area of excited-state research.
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Affiliation(s)
- Yifei Zhu
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou, 510006, P. R. China
- School of Environment, South China Normal University, Guangzhou, 510006, P. R. China
| | - Mengge Li
- School of Environment, South China Normal University, Guangzhou, 510006, P. R. China
| | - Chao Xu
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou, 510006, P. R. China
- School of Environment, South China Normal University, Guangzhou, 510006, P. R. China
| | - Zhenggang Lan
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou, 510006, P. R. China.
- School of Environment, South China Normal University, Guangzhou, 510006, P. R. China.
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5
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Kalasin S, Surareungchai W. Artificial intelligence-aiding lab-on-a-chip workforce designed oral [3.1.0] bi and [4.2.0] tricyclic catalytic interceptors inhibiting multiple SARS-CoV-2 protomers assisted by double-shell deep learning. RSC Adv 2024; 14:26897-26910. [PMID: 39193274 PMCID: PMC11347926 DOI: 10.1039/d4ra03965c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Accepted: 08/20/2024] [Indexed: 08/29/2024] Open
Abstract
While each massive pandemic has claimed the lives of millions of vulnerable populations over the centuries, one limitation exists: that the Edisonian approach (human-directed with trial errors) relies on repurposing pharmaceuticals, designing drugs, and herbal remedies with the violation of Lipinski's rule of five druglikeness. It may lead to adverse health effects with long-term health multimorbidity. Nevertheless, declining birth rates and aging populations will likely cause a shift in society due to a shortage of a scientific workforce to defend against the next pandemic incursion. The challenge of combating the ongoing post-COVID-19 pandemic has been exacerbated by the lack of gold standard drugs to deactivate multiple SARS-CoV-2 protein targets. Meanwhile, there are three FDA-approved antivirals, Remdesivir, Molnupiravir, and Paxlovid, with moderate clinical efficacy and drug resistance. There is a pressing need for additional antivirals and prepared omics technology to combat the current and future devastating coronavirus pandemics. While there is a limitation of existing contemporary inhibitors to deactivate viral RNA replication with minimal rotational bonds, one strategy is to create Lipinski inhibitors with less than 10 rotational bonds and precise halogen bond placement to destabilize multiple viral protomers. This work describes the efforts to design gold-standard oral inhibitors of bi- and tri-cyclic catalytic interceptors with electrophilic heads using double-shell deep learning. Here, KS1 with and KS2 compounds designed by lab-on-a-chip technology attain 5-fold novel filtered-Lipinski, GHOSE, VEBER, EGAN, and MUEGGE druglikeness. The graph neural network (GNN) relies on module-initiation, expansion, relabeling atom index, and termination (METORITE) iterations, while the deep neural network (DNN) engages pinning, extraction, convolution, pooling, and flattening (PROOF) operations. The cyclic compound's specific halogen atom location enhances the nitrile catalytic head, which deactivates several viral protein targets. Initiating this lab-on-a-chip that is not susceptible to the aging process for creating clinical compounds can leverage a new path to many valuable drugs with speedy oral drug discovery, especially to defend the loss of vulnerable population and prevent multimorbidity that is susceptible to hidden viral persistence in the continuing aging times.
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Affiliation(s)
- Surachate Kalasin
- Faculty of Science and Nanoscience & Nanotechnology Graduate Program, King Mongkut's University of Technology Thonburi 10140 Thailand
| | - Werasak Surareungchai
- Faculty of Science and Nanoscience & Nanotechnology Graduate Program, King Mongkut's University of Technology Thonburi 10140 Thailand
- Pilot Plant Research and Development Laboratory, King Mongkut's University of Technology Thonburi 10150 Bangkok Thailand
- School of Bioresource and Technology, King Mongkut's University of Technology Thonburi 10150 Bangkok Thailand
- Analytical Sciences and National Doping Test Institute, Mahidol University Bangkok 10400 Thailand
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6
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Mo W, Ni S, Zhou M, Wen J, Qi D, Huang J, Yang Y, Xu Y, Wang X, Zhao Z. An electron density clustering based adaptive segmentation method for protein Raman spectrum calculation. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 314:124155. [PMID: 38552542 DOI: 10.1016/j.saa.2024.124155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/10/2024] [Accepted: 03/12/2024] [Indexed: 04/20/2024]
Abstract
Raman spectroscopy is a powerful technique for protein detection, but the calculation of Raman spectrum is a longstanding challenging problem due to the large sizes and complex structures of protein molecules. Dividing proteins into fragments can greatly accelerate the calculation, but this usually introduces large errors originating from ignored interactions between fragments into obtained spectra. In this paper, we proposed a new adaptive segmentation method based on the strength of interactions and molecular shapes and structures, i.e., electron density clustering, to divide proteins. It can reduce errors of obtained Raman spectra by about 20% compared to the uniform segmentation method without a significant increase in computational cost. This method can facilitate the validation and analysis of detected Raman spectra of proteins and promote the application of Raman spectroscopy in biological detection.
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Affiliation(s)
- Wenbo Mo
- National Key Laboratory of Plasma Physics, Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China; Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Shuang Ni
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Minjie Zhou
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Jiaxing Wen
- National Key Laboratory of Plasma Physics, Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Daojian Qi
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Jinglin Huang
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Yue Yang
- National Key Laboratory of Plasma Physics, Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Yang Xu
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Xuewu Wang
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Zongqing Zhao
- National Key Laboratory of Plasma Physics, Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China.
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7
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Ma H, Pan SQ, Wang WL, Yue X, Xi XH, Yan S, Wu DY, Wang X, Liu G, Ren B. Surface-Enhanced Raman Spectroscopy: Current Understanding, Challenges, and Opportunities. ACS NANO 2024; 18:14000-14019. [PMID: 38764194 DOI: 10.1021/acsnano.4c02670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2024]
Abstract
While surface-enhanced Raman spectroscopy (SERS) has experienced substantial advancements since its discovery in the 1970s, it is an opportunity to celebrate achievements, consider ongoing endeavors, and anticipate the future trajectory of SERS. In this perspective, we encapsulate the latest breakthroughs in comprehending the electromagnetic enhancement mechanisms of SERS, and revisit CT mechanisms of semiconductors. We then summarize the strategies to improve sensitivity, selectivity, and reliability. After addressing experimental advancements, we comprehensively survey the progress on spectrum-structure correlation of SERS showcasing their important role in promoting SERS development. Finally, we anticipate forthcoming directions and opportunities, especially in deepening our insights into chemical or biological processes and establishing a clear spectrum-structure correlation.
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Affiliation(s)
- Hao Ma
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Si-Qi Pan
- State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, Xiamen University, Xiamen 361102, China
| | - Wei-Li Wang
- State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, Xiamen University, Xiamen 361102, China
| | - Xiaxia Yue
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Xiao-Han Xi
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Sen Yan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - De-Yin Wu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Xiang Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Guokun Liu
- State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, Xiamen University, Xiamen 361102, China
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (i-ChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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8
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Lu XY, Wu HP, Ma H, Li H, Li J, Liu YT, Pan ZY, Xie Y, Wang L, Ren B, Liu GK. Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives. Anal Chem 2024; 96:7959-7975. [PMID: 38662943 DOI: 10.1021/acs.analchem.4c01639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.
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Affiliation(s)
- Xin-Yu Lu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Hao-Ping Wu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, P. R. China
| | - Hao Ma
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Hui Li
- Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen 361005, P. R. China
| | - Jia Li
- Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, P. R. China
| | - Yan-Ti Liu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Zheng-Yan Pan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Yi Xie
- School of Informatics, Xiamen University, Xiamen 361005, P. R. China
| | - Lei Wang
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361005, P. R. China
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Guo-Kun Liu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, P. R. China
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9
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Duan S, Tian G, Luo Y. Theoretical and computational methods for tip- and surface-enhanced Raman scattering. Chem Soc Rev 2024; 53:5083-5117. [PMID: 38596836 DOI: 10.1039/d3cs01070h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Raman spectroscopy is a versatile tool for acquiring molecular structure information. The incorporation of plasmonic fields has significantly enhanced the sensitivity and resolution of surface-enhanced Raman scattering (SERS) and tip-enhanced Raman spectroscopy (TERS). The strong spatial confinement effect of plasmonic fields has challenged the conventional Raman theory, in which a plane wave approximation for the light has been adopted. In this review, we comprehensively survey the progress of a generalized theory for SERS and TERS in the framework of effective field Hamiltonian (EFH). With this approach, all characteristics of localized plasmonic fields can be well taken into account. By employing EFH, quantitative simulations at the first-principles level for state-of-the-art experimental observations have been achieved, revealing the underlying intrinsic physics in the measurements. The predictive power of EFH is demonstrated by several new phenomena generated from the intrinsic spatial, momentum, time, and energy structures of the localized plasmonic field. The corresponding experimental verifications are also carried out briefly. A comprehensive computational package for modeling of SERS and TERS at the first-principles level is introduced. Finally, we provide an outlook on the future developments of theory and experiments for SERS and TERS.
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Affiliation(s)
- Sai Duan
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai 200433, China.
| | - Guangjun Tian
- State Key Laboratory of Metastable Materials Science & Technology and Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao 066004, China
| | - Yi Luo
- Hefei National Research Center for Physical Science at the Microscale and Synergetic Innovation Center of Quantum Information & Quantum Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
- Hefei National Laboratory, University of Science and Technology of China, Hefei, 230088, China
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10
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Zhao D, Zhao Y, Xu E, Liu W, Ayers PW, Liu S, Chen D. Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates. J Chem Theory Comput 2024; 20:2655-2665. [PMID: 38441881 DOI: 10.1021/acs.jctc.3c01415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Simultaneous prediction of the molecular response properties, such as polarizability and the NMR shielding constant, at a low computational cost is an unresolved issue. We propose to combine a linear-scaling generalized energy-based fragmentation (GEBF) method and deep learning (DL) with both molecular and atomic information-theoretic approach (ITA) quantities as effective descriptors. In GEBF, the total molecular polarizability can be assembled as a linear combination of the corresponding quantities calculated from a set of small embedded subsystems in GEBF. In the new GEBF-DL(ITA) protocol, one can predict subsystem polarizabilities based on the corresponding molecular wave function (thus electron density and ITA quantities) and DL model rather than calculate them from the computationally intensive coupled-perturbed Hartree-Fock or Kohn-Sham equations and finally obtain the total molecular polarizability via a linear combination equation. As a proof-of-concept application, we predict the molecular polarizabilities of large proteins and protein aggregates. GEBF-DL(ITA) is shown to be as accurate enough as GEBF, with mean absolute percentage error <1%. For the largest protein aggregate (>4000 atoms), GEBF-DL(ITA) gains a speedup ratio of 3 compared with GEBF. It is anticipated that when more advanced electronic structure methods are used, this advantage will be more appealing. Moreover, one can also predict the NMR chemical shieldings of proteins with reasonably good accuracy. Overall, the cost-efficient GEBF-DL(ITA) protocol should be a robust theoretical tool for simultaneously predicting polarizabilities and NMR shieldings of large systems.
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Affiliation(s)
- Dongbo Zhao
- Institute of Biomedical Research, Yunnan University, Kunming, Yunnan 650500, P. R. China
| | - Yilin Zhao
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton ONL8S4M1, Canada
| | - Enhua Xu
- Graduate School of System Informatics, Kobe University, Nada-ku, Kobe, Hyogo 657-8501, Japan
| | - Wenqi Liu
- Institute of Biomedical Research, Yunnan University, Kunming, Yunnan 650500, P. R. China
| | - Paul W Ayers
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton ONL8S4M1, Canada
| | - Shubin Liu
- Research Computing Center, University of North Carolina, Chapel Hill, North Carolina 27599-3420, United States
- Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599-3290, United States
| | - Dahua Chen
- Institute of Biomedical Research, Yunnan University, Kunming, Yunnan 650500, P. R. China
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11
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Dral PO. AI in computational chemistry through the lens of a decade-long journey. Chem Commun (Camb) 2024; 60:3240-3258. [PMID: 38444290 DOI: 10.1039/d4cc00010b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
This article gives a perspective on the progress of AI tools in computational chemistry through the lens of the author's decade-long contributions put in the wider context of the trends in this rapidly expanding field. This progress over the last decade is tremendous: while a decade ago we had a glimpse of what was to come through many proof-of-concept studies, now we witness the emergence of many AI-based computational chemistry tools that are mature enough to make faster and more accurate simulations increasingly routine. Such simulations in turn allow us to validate and even revise experimental results, deepen our understanding of the physicochemical processes in nature, and design better materials, devices, and drugs. The rapid introduction of powerful AI tools gives rise to unique challenges and opportunities that are discussed in this article too.
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Affiliation(s)
- Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China.
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12
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Tetsassi Feugmo CG. Accurately predicting molecular spectra with deep learning. NATURE COMPUTATIONAL SCIENCE 2023; 3:918-919. [PMID: 38177595 DOI: 10.1038/s43588-023-00553-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
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