导师风采
邵学广
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  • 教授
  • 导师类别:博士生导师
  • 性别: 男
  • 学历:博士研究生
  • 学位:博士

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Contact Information

  • 所属院系:化学学院
  • 所属专业: 分析化学
  • 邮箱 : xshao@nankai.edu.cn
  • 工作电话 : 022-23503430

个人简介

Personal Profile

邵学广:博士,南开大学教授,博士生导师。《Chemometrics and Intelligent Laboratory Systems》、《高等学校化学学报》、《分析化学》等多种期刊的编委会委员、中国化学会理事、计算机化学专业委员会主任委员、有机分析专业委员会委员、中国仪器仪表学会近红外光谱分会副理事长、天津市分析测试协会副理事长。2002年获教育部第三届高校青年教师奖,2003年获国家自然科学基金委杰出青年基金,2010年获宝钢优秀教师奖,2012年获国务院政府特殊津贴,2018年获中国仪器仪表学会“陆婉珍近红外光谱科技奖”。

主要从事化学计量学及其在化学领域中的应用研究。先后开展了化学因子分析、优化算法、免疫算法、小波分析等方面的研究工作,建立了一系列复杂分析化学信号分析方法以及用于近红外光谱信号处理和建模的化学计量学方法,为复杂体系近红外光谱快速分析和产品质量评价等建立了新方法。近年来主要开展温控近红外光谱、水光谱探针等方面的研究工作,开拓了化学计量学的应用领域以及近红外光谱的研究领域。在国内外学术期刊上发表SCI论文300余篇,编著、翻译或合作出版学术著作5部。


  • 研究方向Research Directions
光谱分析,化学信息学,人工智能
2. 机电结构优化与控制 研究内容:在对机电结构进行分析和优化的基础上,运用控制理论进行结构参数的调整,使结构性能满足设计要求。1. 仿生结构材料拓扑优化设计, 仿生机械设计 研究内容:以仿生结构为研究对象,运用连续体结构拓扑优化设计理论和方法,对多相仿生结构(机构)材料进行2. 机电结构优化与控制 研究内容:在对机电结构进行分析和优化的基础上,运用控制理论进行结构参数的调整,使结构性能满足设计要求。1. 仿生结构材料拓扑优化设计, 仿生机械设计 研究内容:以仿生结构为研究对象,运用连续体结构拓扑优化设计理论和方法,对多相仿生结构(机构)材料进行整体布局设计。 整体布局设计。
团队展示

https://chinfo.nankai.edu.cn图片1


项目情况

(1)    2024-2027,国家自然科学基金,面上项目,水光谱与超声生物成像人工智能方法研究,项目负责人。

(2)    2024-2026,国家重点研发计划,荧光纳米复合探针对病原菌特异性识别新策略及机制研究,主要参加。

(3)   2024-2026,国家自然科学基金,中韩国际合作与交流项目,高维光谱数据测量与人工智能分析方法研究,项目负责人。

(4)   2022-2025,国家自然科学基金,面上项目,近红外水光谱探针与化学计量学信息提取方法研究,项目负责人。

(5)   2018-2021,国家自然科学基金,面上项目,近红外水光谱组学方法与应用研究,项目负责人。

(6)   2015-2018,国家自然科学基金,面上项目,温控近红外光谱及相关的化学计量学方法研究,项目负责人。

(7)   2014-2017,国家重大科学仪器设备开发专项,光栅型近红外分析仪及其共用模型开发和应用-(基于高维形象几何分析的NIR分析技术研究与软件开发),主要参加。

(8)   2012-2015,国家自然科学基金,面上项目,复杂体系GC-MS高通量分析方法研究,项目负责人。

(9)   2009-2012,国家自然科学基金,重点项目,复杂基质样品的稳健分析方法研究,项目负责人。

(10) 2008-2010,科技部国际合作专项,肿瘤拉曼散射诊断仪信号处理技术研究,项目负责人。


科研项目

(1)   2024-2027,国家自然科学基金,面上项目,水光谱与超声生物成像人工智能方法研究,项目负责人。

(2)   2024-2026,国家重点研发计划,荧光纳米复合探针对病原菌特异性识别新策略及机制研究,主要参加。

(3)  2024-2026,国家自然科学基金,中韩国际合作与交流项目,高维光谱数据测量与人工智能分析方法研究,项目负责人。

(4)  2022-2025,国家自然科学基金,面上项目,近红外水光谱探针与化学计量学信息提取方法研究,项目负责人。

(5)  2018-2021,国家自然科学基金,面上项目,近红外水光谱组学方法与应用研究,项目负责人。

(6)  2015-2018,国家自然科学基金,面上项目,温控近红外光谱及相关的化学计量学方法研究,项目负责人。

(7)  2014-2017,国家重大科学仪器设备开发专项,光栅型近红外分析仪及其共用模型开发和应用-(基于高维形象几何分析的NIR分析技术研究与软件开发),主要参加。

(8)  2012-2015,国家自然科学基金,面上项目,复杂体系GC-MS高通量分析方法研究,项目负责人。

(9)  2009-2012,国家自然科学基金,重点项目,复杂基质样品的稳健分析方法研究,项目负责人。

(10)2008-2010,科技部国际合作专项,肿瘤拉曼散射诊断仪信号处理技术研究,项目负责人。


研究成果

2020年以来主要成果:

(1)    Ma,B.; Chen, N. N.; Cai, W. S.; Shao, X. G.* Understanding the proteinconformation transition within polymer hydrogels using a near-infrared waterspectroscopy probe. Int. J. Biol. Macromol. 2025, 290, 138995.

(2)    Su,C. L.; Cai, W. S.; Shao, X. G..* Water as a probe for the temperature-inducedself-assembly transition of an amphiphilic copolymer. Chin. Chem. Lett.2025, 36, 110095.

(3)    Zhou,H. X.; Fu, H. H.;* Shao, X. G.;* Cai, W. S.* Identification of novel inhibitorsfor epidermal growth factor receptor tyrosine kinase using absolute bindingfree-energy simulations. Int. J. Biol. Macromol. 2025, 304, 140989.

(4)    Liu,X.Y.; An, H. L.; Cai, W. S.*; Shao, X. G.* Deep Learning in Spectral Analysis:Modeling and Imaging. TrAC, Trends Anal.Chem. 2024, 172, 117612.

(5)    An,H. L.; Liu, X. Y.; Cai, W. S.; Shao, X. G.* Explainable Graph Neural Networkswith Data Augmentation for Predicting pKa of C−H Acids. J. Chem. Inf. Model.2024, 64(7), 2383−2392.

(6)    Duan,C. S.;# Liu, X. Y.;# Cai, W. S.; Shao, X. G.*Interpretable perturbator for variable selection in near-infrared spectralanalysis. J. Chem. Inf. Model. 2024, 64(7), 2508−2514.

(7)    Liu,X. Y.; Xing, J. Y.; Fu, H. H.; Shao, X. G.;* Cai, W. S.* Analyzing MolecularDynamics Trajectories Thermodynamically through Artificial Intelligence. J.Chem. Theory Comput. 2024, 20(2), 665−676.

(8)    Shao, D. H.; Zhang, Z. T.; Liu, X. Y.; Fu, H. H.;* Shao, X. G.;* Cai, W.S.* Screening Fast-mode Motion in Collective Variable Discovery for BiochemicalProcesses. J. Chem. Theory Comput. 2024, 20(23), 10393-10405.

(9)    Fu,H. H.; Bian, H. W.; Shao, X. G.;* Cai, W. S.* Collective Variable-BasedEnhanced Sampling: From Human Learning to Machine Learning. J. Phys. Chem.Lett. 2024, 15(6), 1774−1783.

(10)  Hao, Y. L.; Liu, X. Y.; Fu, H. H.; Shao, X. G.*; Cai, W. S.*PGAT-ABPp: harnessing protein language models and graph attention networks forantibacterial peptide identification with remarkable accuracy. Bioinformatics,2024, 40(8), btae497.

(11)  Su, C. L; Wang, H. P.; Cai, W. S.*;Shao, X. G.* Ice growth inhibition by poly(vinyl alcohol): Insights fromnear-infrared spectroscopy and molecular dynamics simulation. J. Mol. Liq.2024, 402, 124795.

(12)  An, H. L.; Liu, X. Y.; Cai, W. S.*;Shao, X. G.* AttenGpKa: A Universal Predictor of Solvation Acidity Using GraphNeural Network and Molecular Topology. J. Chem. Inf. Model. 2024, 64(14),5480-5491.

(13)  Wang, H. P.; Han, L.; Cai, W. S.; Shao,X. G.* Chemometrics: A Vital Implement for Understanding the Water Structuresby Near-Infrared Spectroscopy. J. Chemometr. 2024; 38(12), e3631.

(14)  Liu, X. Y.; Duan, C. S.; Cai, W. S.;Shao, X. G.* Unmixing Autoencoder for Image Reconstruction from HyperspectralData. Anal. Chem. 2024, 96(52), 20354-20361.

(15)  Han, L.; Wang, H. P.; Cai, W.; Shao, X.G.* Mechanism of Binding of Polyproline to Ice via Interfacial Water: AnExperimental and Theoretical Study. J. Phys. Chem. Lett. 2023, 14,4127−4133.

(16)  Han, L.; Sun, Y.; Wang, Y.; Fu, H. H.;Duan, C. S.; Wang, M.; Cai, W. S.; Shao, X. G.* Ultra-high resolutionnear-infrared spectrum by wavelet packet transform revealing the hydrogen bondinteractions. Spectrochim. Acta, Part A, 2023, 289, 122233.

(17)  Wang, M.; An, H. L.; Cai, W. S.; Shao, X.G.* Wavelet Transform Makes Water an Outstanding Near-Infrared SpectroscopicProbe. Chemosensors, 2023, 11(1), 37.

(18)  Xu, X.; Han, L.; Zheng, Z.;* Zhao, R.;Li, L. J.; Shao, X. G.; Li.; G. Y.* Composite Multidimensional IonMobility-Mass Spectrometry for Improved Differentiation of StereochemicalModifications. Anal. Chem. 2023, 95(4), 2221−2228.

(19)  Han, L.; Sun, Y.; Cai, W. S.; Shao, X.G. Seeking the structure of water from the combination of bending andstretching vibrations in near infrared spectra. J. Near Infrared Spec.2023, 31(4), 204-210.

(20)  Li, J. N.; Liang, F. F.; Han, L.; Yu,X. X.; Liu, D. B.; Cai, W. S.; Shao, X. G. Determination of Extra- andIntra-Cellular pH Using Characteristic Absorption of Water by Near-InfraredSpectroscopy. Chemosensors, 2023, 11(8), 425.

(21)  Fu, H. H.; Chipot, C.; Shao, X. G.;*Cai, W. S.* Achieving Accurate Standard Protein−Protein Binding Free EnergyCalculations through the Geometrical Route and Ergodic Sampling. J. Chem.Inf. Model. 2023, 63(8), 2512−2519.

(22)  Su, T.; Sun, Y.; Han, L.; Cai, W. S;Shao, X. G.* Revealing the interactions of water with cryoprotectant andprotein by near–infrared spectroscopy. Spectrochim. Acta, Part A, 2022,266, 120417.

(23)  Wang, S. Y.; Wang, M.; Han, L.; Sun,Y.; Cai, W. S; Shao, X. G.* Insight into the stability of protein in confinedenvironment through analyzing the structure of water by temperature-dependentnear-infrared spectroscopy. Spectrochim. Acta, Part A, 2022, 267,120581.

(24)  Chen, H. C.; Liu, H.; Feng, H. Y.; Fu, H. H.; Cai, W. S.;* Shao, X.G.;* Chipot, C.* MLCV: Bridging Machine-Learning-Based Dimensionality Reductionand Free-Energy Calculation. J. Chem.Inf. Model. 2022, 62, 1−8.

(25)  Fu, H. H.; Zhou, Y.; Jing,X.; Shao, X. G.;* Cai, W. S.* Meta-Analysis Reveals That Absolute BindingFree-Energy Calculations Approach Chemical Accuracy. J.Med. Chem. 2022, 65, 12970−12978.

(26)  Wang, K.; Shao, X. G.;* Cai, W. S.*Binding Models of Aβ42 Peptide with Membranes Explored by MolecularSimulations. J. Chem. Inf. Model. 2022, 62, 6482−6493.

(27)  Liu, H.; Fu, H. H.; Chipot, C.;* Shao,X. G.;* Cai, W. S.* Accurate Description of Solvent-Exposed Salt Bridges with aNon-Polarizable Force Field Incorporating Solvent Effects. J. Chem. Inf. Model. 2022, 62, 3863−3873.

(28)  Cui, S. L.; Zhang, W. J.; Shao, X. G.;*Cai, W. S.* Hyperactive Antifreeze Proteins Promote Ice Growth before Bindingto It. J. Chem. Inf. Model. 2022, 62, 5165−5174.

(29)  Zong Z. Y.; Mazurkewich S.; Pereira C.S.; Fu, H. H.; Cai, W. S.; Shao, X. G.; Skaf, M. S.; Larsbrink, J.; Lo LeggioL. Mechanism and biomass association of glucuronoyl esterase: An α/β hydrolasewith potential in biomass conversion. Nat. Commun. 2022, 13, 1449.

(30)  Duan, C. S.; Liu, X. Y.; Cai, W. S.;Shao. X. G.* Spectral Encoder to Extract the Features of Near-Infrared Spectrafor Multivariate Calibration. J. Chem. Inf. Model. 2022, 62(16),3695−3703.

(31)  Han, L.; Sun, Y.; Wang, S. Y.; Su, T.;Cai, W. S.; Shao, X. G.* Understanding the water structures by near-infraredand Raman spectroscopy. J Raman Spectrosc. 2022,53(10), 1686-1693.

(32)  Fu, H. H.; Chen, H. C.; Blazhynska, M.;Goulard Coderc de Lacam, E.; Szczepaniak, F.; Pavlova, A.; Shao, X. G.;Gumbart, J. C.; Dehez, F.; Roux, B.; Cai, W.S.;* Chipot, C.* AccurateDetermination of Protein: Ligand Standard Binding Free Energies from MolecularDynamics Simulations. Nat.Protoc. 2022,17(4), 1114-1141.

(33)  刘煦阳,段潮舒,蔡文生,邵学广*,可解释深度学习在光谱和医学影像分析中的应用,化学进展,2022, 34(12),2561-2572.

(34)  Zhang, H.; Guo, Y. C.; Chipot, C.; Cai,W. S.;* Shao, X. G.* Nanomachine-Assisted Ion Transport Across Membranes: FromMechanism to Rational Design and Applications. J. Phys. Chem. Lett.2021, 12(13), 3281-3287.

(35)  Fu, H. H.; Chipot, C.; Cai, W. S.;*Shao, X. G.* Repurposing Existing Molecular Machines through AccurateRegulation of Cooperative Motions. J. Phys. Chem. Lett. 2021, 12(1),613-619.

(36)  Fu, H. H.; Chen, H. C.; Cai, W. S.;*Shao, X. G.;* Chipot, C.* BFEE2: Automated, Streamlined, and Accurate AbsoluteBinding Free-Energy Calculations. J.Chem. Inf. Model. 2021, 61(5), 2116-2123.

(37)  Chen, H. C.; Fu, H. H.; Chipot, C.;* Shao, X. G.;* Cai W. S.*Overcoming Free-Energy Barriers with a Seamless Combination of a Biasing Forceand a Collective Variable-Independent Boost Potential. J. Chem. Theory Comput. 2021, 17, 3886−3894.

(38)  Liu, H.; Fu, H. H.; Chipot,C.;* Shao, X. G.;* Cai, W. S.* Accuracy of AlternateNon-Polarizable Force Fields for the Determination of Protein–Ligand BindingAffinities Dominated by Cation–π Interactions. J. Chem. Theory Comput. 2021, 17, 3908−3915.

(39)  Zhang, J.; Guo, C.; Cai, W. S.; Shao,X. G*. Direct non-trilinear decomposition for analyzing high-dimensional datawith imperfect trilinearity. Chemom.Intell. Lab. Syst. 2021, 210, 104244.

(40)  Ma, B.; Wang, L.; Han, L.; Cai, W. S.;Shao, X. G.* Understanding the effect of urea on the phase transition ofpoly(N-isopropylacrylamide) in aqueous solution by temperature-dependentnear-infrared spectroscopy. Spectrochim.Acta, Part A 2021, 253, 119573.

(41)  Sun, Y.; Li, M.; Cai, W. S.; Shao, X. G.* Interaction between tauand water during the induced aggregation revealed by near-infraredspectroscopy. Spectrochim. Acta, Part A. 2020, 230, 118046.

(42)  Sun, Y.; Cui, X. Y.; Cai, W. S.; Shao, X. G.* Understanding thecomplexity of the structures in alcohol solutions by temperature–dependent near–infraredspectroscopy. Spectrochim. Acta, Part A. 2020, 229, 117864.

(43)  Han, L.; Cui, X. Y.; Cai, W. S.; Shao, X. G.* Three–levelsimultaneous component analysis for analyzing the near–infrared spectra ofaqueous solutions under multiple perturbations. Talanta 2020, 217,121036.

(44)  Tan, J. H.; Sun, Y.; Ma, L.; Feng, H. Y.; Guo, Y. C.; Cai, W. S.;Shao, X. G. Knowledge-based genetic algorithm for resolving the near-infraredspectrum and understanding the water structures in aqueous solution. Chemometr.Intell. Lab. Syst. 2020, 206, 104150.

(45)  Zhang, C.; Cui, X. Y.; Yang, J.; Shao, X. G.; Zhang, Y. Y.; Liu, D.B. Stimulus-responsive surface-enhanced Raman scattering: A “Trojan horse”strategy for precision molecular diagnosis of cancer. Chem. Sci. 2020, 11, 6111–6120.

(46)  Liu, H.; Fu, H. H.; Shao, X. G.;* Cai, W. S.;* Chipot, C.* AccurateDescription of Cation−π Interactions in Proteins with a Nonpolarizable ForceField at No Additional Cost. J. Chem. Theory Comput. 2020, 16,6397−6407.

(47)  Fu, H. H.; Chen, H. C.; Wang, X. A.; Chai, H.; Shao, X. G.;* Cai, W.S.;* Chipot, C.* Finding an Optimal Pathway on a Multidimensional Free-EnergyLandscape. J. Chem. Inf. Model. 2020, 60, 5366–5374.

 

发明专利:

(1)   邵学广,段潮舒,蔡文生,基于多元光学计算的近红外光谱测试方法,2022-12-13,中国,ZL202011200158.5

(2)   邵学广,段潮舒,蔡文生,基于模式序列生成近红外光谱数据的方法,2022-12-27,中国,ZL202011181348.7


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