https://chinfo.nankai.edu.cn

(1) 国家自然科学基金, 面上项目，22373051, 基于机器学习的抗菌肽从头设计, 2024-01-01 至2027-12-31, 项目负责人。

(2) 国家自然科学基金, 面上项目，22073050, 基于人工智能的重要性采样方法及应用研究,2021-01-01 至 2024-12-31, 项目负责人。

(3) 物质绿色创造与制造海河实验室, 自由探索项目, ZYTS202105, 基于分子模拟的大分子-大分子相互作用的理论表征方法, 2022-01 至 2022-12, 项目负责人。

(4) 国家自然科学基金, 面上项目, 21773125, 面向计算机辅助分子机器设计的模拟方法和应用研究, 2018-01-01 至 2021-12-31, 项目负责人。

(5) 天津市自然科学基金, 面上项目, 18JCYBJC20500, 重要性采样方法用于纳米机器中复杂分子运动的模拟研究, 2018-04 至 2021-03, 项目负责人。

(6) 国家自然科学基金，面上项目，21373117，基于环糊精构筑的分子器件的模拟研究，2014-01至2017-12，项目负责人。

(1) 国家自然科学基金, 面上项目，22373051, 基于机器学习的抗菌肽从头设计, 2024-01-01 至2027-12-31, 项目负责人。

(2) 国家自然科学基金, 面上项目，22073050, 基于人工智能的重要性采样方法及应用研究,2021-01-01 至 2024-12-31, 项目负责人。

(3) 物质绿色创造与制造海河实验室, 自由探索项目, ZYTS202105, 基于分子模拟的大分子-大分子相互作用的理论表征方法, 2022-01 至 2022-12, 项目负责人。

(4) 国家自然科学基金, 面上项目, 21773125, 面向计算机辅助分子机器设计的模拟方法和应用研究, 2018-01-01 至 2021-12-31, 项目负责人。

(5) 天津市自然科学基金, 面上项目, 18JCYBJC20500, 重要性采样方法用于纳米机器中复杂分子运动的模拟研究, 2018-04 至 2021-03, 项目负责人。

(6) 国家自然科学基金，面上项目，21373117，基于环糊精构筑的分子器件的模拟研究，2014-01至2017-12，项目负责人。

2020年以来主要成果 （完整成果目录请访问实验室主页：https://chinfo.nankai.edu.cn）：

(1)    Zhou,H. X.; Fu, H. H.;* Shao, X. G.;* Cai, W. S.* Identification of novelinhibitors for epidermal growth factor receptor tyrosine kinase using absolutebinding free-energy simulations. Int. J. Biol. Macromol. 2025,304, 140989.

(2)    Liu, H.; Ding, Y.; Mazurkewich,S.; Pei, W. W.; Wei, X. X.; Larsbrink, J.; Chipot, C.*; Hong, Z. Y.*; Cai,W. S.*; Zong, Z. Y.* Boosting Enzyme Activity in Biomass Conversion byModulating the Hydrolysis Process of Cellobiohydrolases. ACS Catal. 2024,14(21), 16044–16054.

(3)    Liu,X. Y.; Xing, J. Y.; Fu, H. H.; Shao, X. G.;* Cai, W. S.* AnalyzingMolecular Dynamics Trajectories Thermodynamically through ArtificialIntelligence. J. Chem. Theory Comput. 2024, 20(2), 665−676.

(4)     Shao, D. H.; Zhang, Z. T.; Liu, X. Y.;Fu, H. H.;* Shao, X. G.;* Cai, W. S.* Screening Fast-mode Motion inCollective Variable Discovery for Biochemical Processes. J. Chem. TheoryComput. 2024, 20(23),10393-10405.

(5)    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.

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

(7)    Hao, Y. L.; Liu, X. Y.; Fu, H.H.; Shao, X. G.*; Cai, W. S.* PGAT-ABPp: harnessing protein languagemodels and graph attention networks for antibacterial peptide identificationwith remarkable accuracy. Bioinformatics, 2024, 40(8), btae497.

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

(9)    Fu, H. H.; Liu, H.;Xing, J. Y.; Zhao, T.; Shao, X. G.;* Cai, W. S.* Deep-Learning-Assisted Enhanced Sampling for Exploring MolecularConformational Changes. J. Phys. Chem. B. 2023, 127(46),9926−9935.

(10) Zhou,H. X.; Fu, H. H.*; Shao, X. G.; Cai, W. S.* Binding Thermodynamics ofFourth-Generation EGFR Inhibitors Revealed by Absolute Binding Free EnergyCalculations. J. Chem. Inf. Model. 2023, 63(24), 7837-7846.

(11) 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.* Accurate Determination of Protein: Ligand Standard Binding FreeEnergies from Molecular Dynamics Simulations. Nat.Protoc. 2022,17(4), 1114-1141.

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

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

(14) Wang,K.; Shao, X. G.;* Cai, W. S.* Binding Models of Aβ42 Peptide withMembranes Explored by Molecular Simulations. J. Chem. Inf. Model. 2022,62, 6482−6493.

(15) Liu,H.; Fu, H. H.; Chipot, C.;* Shao, X. G.;* Cai, W. S.* AccurateDescription of Solvent-Exposed Salt Bridges with a Non-Polarizable Force FieldIncorporating Solvent Effects. J. Chem. Inf. Model. 2022, 62, 3863−3873.

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

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

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

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

(20)  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.

(21) Liu, H.; Fu, H. H.; Chipot, C.;* Shao, X. G.;* Cai,W. S.* Accuracy of Alternate Non-Polarizable Force Fields for theDetermination of Protein–Ligand Binding Affinities Dominated by Cation–πInteractions. J. Chem. Theory Comput.2021, 17, 3908−3915.

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

(23)  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 MultidimensionalFree-Energy Landscape. J. Chem. Inf. Model. 2020, 60, 5366–5374.