CHIANG Ying-Chih

Adjunct Assistant Professor

教育背景

Ph.D. in Chemistry (University of Heidelberg, Germany)

M.Sc. in Chemistry (National Taiwan University, Taiwan)

B.Sc. in Chemistry (National Taiwan University, Taiwan)

研究领域
Theoretical chemistry, computational biology, bioinformatics, statistical mechanics
学术领域
Physics, Chemistry, Artificial Intelligence and Robotics
个人网站
https://mypage.cuhk.edu.cn/academics/chiangyc/about.html
电子邮件
chiangyc@cuhk.edu.cn
个人简介

Ying-Chih Chiang is a computational chemist with extensive research experience spanning a wide range of cutting-edge topics in molecular dynamics, statistical mechanics, and computer-aided drug design. Ying-Chih pursued her PhD studies in Heidelberg, Germany, focusing on atomic physics in the gas phase. Her research delved into the influence of nuclear motion during electronic decay processes, reshaping our understanding of the linewidth measured in associated spectra.

Following her doctoral studies, Ying-Chih transitioned her research focus to biophysics. She conducted research as a postdoctoral researcher at The Chinese University of Hong Kong before becoming a Newton International Fellow at the University of Southampton. During this period, Ying-Chih developed a novel method for post-processing ligand trajectories to calculate the free energy difference between a ligand and its derivative upon binding to a protein. Additionally, she explored the impact of enzyme evolution on substrate binding, using molecular dynamics simulations. Over time, her research shifted towards targets associated with antimicrobial resistance.

Currently serving as an Assistant Professor at the Kobilka Institute of Innovative Drug Discovery at The Chinese University of Hong Kong, Shenzhen, Ying-Chih is actively involved in pioneering research. Her group developed several machine learning models for antimicrobial peptide classification, investigated receptor-substrate binding through molecular dynamics simulations, and optimized force field parameters for β-lactams using quantum calculations. To date, Ying-Chih has published 42 papers in various peer-reviewed journals, including Nature Structural & Molecular Biology (IF 18.36), Nature Communications (IF 14.7), Briefings in Bioinformatics (IF 13.99), Protein Science (IF 8.0), Analytical Chemistry (IF 8.0), Physical Review Letters (IF 8.1), The Journal of Chemical Physics (IF 4.4), and others.  

学术著作
  1. CapsEnhancer: An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network. L. Yao, P. Xie, J. Guan, C.-R. Chung, Y. Huang, Y. Pang, H. Wu, Y.-C. Chiang, and T.-Y. Lee. J. Chem. Inf. Model. 2024, 64(14): 5725–5736.
  2. Discovery and substrate specificity engineering of nucleotide halogenases. J. Ni, J. Zhuang, Y. Shi, Y.-C. Chiang, and G.-J. Cheng. Nat. Commun. 2024, 15(1): 5254.
  3. AMPActiPred: A three‐stage framework for predicting antibacterial peptides and activity levels with deep forest. L. Yao, J. Guan, P. Xie, C.‐R. Chung, J. Deng, Y. Huang, Y.‐C. Chiang, and Tzong‐Yi Lee. Prot. Sci. 2024, 33(6): e5006.
  4. A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy. J. Guan, L. Yao, P. Xie, C.-R. Chung, Y. Huang, Y.-C. Chiang, and T.-Y. Lee. Brief. Bioinform. 2024, 25(3): bbae208.
  5. Identifying Residues for Substrate Recognition in Human GPAT4 by Molecular Dynamics Simulations. Y. Liu, Y. Xu, Y. Xu, Z. Zhao, G.-J. Cheng, R. Ren, and Y.-C. Chiang. Int. J. Mol. Sci. 2024, 25(7): 3729.
  6. Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation.  L. Yao, J. Guan, W. Li, C.-R. Chung, J. Deng, Y.-C. Chiang, and T.-Y. Lee. Anal. Chem. 2024, 96(4): 1538–1546.
  7. Structural insights into the activation and inhibition of CXC chemokine receptor 3. H. Jiao, B. Pang, A. Liu, Q. Chen, Q. Pan, X. Wang, Y. Xu, Y.-C. Chiang, R. Ren and H. Hu. Nat. Struct. Mol. Biol. 2024, 31(4): 610-620.
  8. Structure basis for the modulation of CXC chemokine receptor 3 by antagonist AMG487. H. Jiao, B. Pang, Y.-C. Chiang, Q. Chen, Q. Pan, R. Ren and H. Hu. Cell Discov. 2023, 9(1): 119.
  9. Predicting Anti-inflammatory Peptides by Ensemble Machine Learning and Deep Learning. J. Guan, L. Yao, C.-R. Chung, P. Xie, Y. Zhang, J. Deng, Y.-C. Chiang, and T.-Y. Lee. J. Chem. Inf. Model. 2023, 63(24): 7886–7898.
  10. DeepAFP: an effective computational framework for identifying antifungal peptides based on deep learning. L. Yao, Y. Zhang, W. Li, C.-R. Chung, J. Guan, W. Zhang, Y.-C. Chiang, and T.-Y. Lee. Protein Sci. 2023, 32(10): e4758.
  11. StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture. J. Guan, L. Yao, C.-R. Chung, Y.-C. Chiang and T.-Y. Lee. Int. J. Mol. Sci. 2023, 24(12): 10348.
  12. ABPCaps: A Novel Capsule Network-Based Method for the Prediction of Antibacterial Peptides. L. Yao, Y. Pang, J. Wan, C.-R. Chung, J. Yu, J. Guan, C. Leung, Y.-C. Chiang and T.-Y. Lee. Appl. Sci. 2023, 13(12): 6965.
  13. Accelerating the discovery of anticancer peptides through deep forest architecture with deep graphical representation. L. Yao, W. Li, Y. Zhang, J. Deng, Y. Pang, Y. Huang, C.-R. Chung, J. Yu, Y.-C. Chiang and T.-Y. Lee.  Int. J. Mol. Sci. 2023, 24(5): 4328.
  14. Exploring the chemical space of CYP17A1 inhibitors using cheminformatics and machine learning. T. Yu, T. Huang, L. Yu, C. Nantasenama, N. Anuwongcharoen, T. Piacham, R. Ren and Y.-C. Chiang. Molecules 2023, 28(4): 1679.
  15. Identification of neurotoxic compounds in cyanobacteria exudate mixtures. Y. Zi, J. R. Barker, H. J. MacIsaac, R. Zhang, R. Gras, Y-C. Chiang, Y. Zhou, F. Lu, W. Cai, C. Sun and X. Chang.  Sci. Total Environ. 2022, 857(Part 2): 159257.
  16. On the force field optimisation of β-lactam cores using the force field Toolkit.  Q. Wu, T. Huang, S. Xia, F. Otto, T.-Y. Li, H.-D. Huang and Y.-C. Chiang.  J. Comput. Aided Mol. Des. 2022, 36(7): 537-547.
  17. dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data.  J.-H. Jhong, L. Yao, Y.  Pang, Z. Li, C.-R. Chung, R. Wang, S. Li, W. Li, M. Luo, R. Ma, Y. Huang, X. Zhu, J. Zhang, H. Feng, Q. Cheng, C. Wang, K. Xi, L.-C. Wu, T.-H. Chang, J.-T. Horng, L. Zhu, Y.-C. Chiang, Z. Wang and T.-Y. Lee.  Nucleic Acids Res. 2022, 50(D1): D460-D470.
  18. Crystal structure of steroid reductase SRD5A reveals conserved steroid reduction mechanism.  YF. Han, Q. Zhuang, B. Sun, WP. Lv, S. Wang, QJ. Xiao, B. Pang, YL. Zhou, FX. Wang, PL. Chi, QS. Wang, Z. Li, LZ. Zhu, FP. Li, D. Deng, Y.-C. Chiang, ZF. Li and RB. Ren.   Nat. Commun. 2021, 12(1): 449.
  19. Structural basis for divergent and convergent evolution of catalytic machineries in plant aromatic amino acid decarboxylase proteins.  M. P. Torrens-Spence, Y.-C. Chiang, T. Smith, M. A. Vicent, Y. Wang and J.-K. Weng.  Proc. Natl. Acad. Sci. U.S.A. 2020, 117(20): 10806.
  20. Molecular Dynamics Simulations of Antibiotic Ceftaroline at The Allosteric Site of Penicillin-Binding Protein 2a (PBP2a).  Y.-C. Chiang, M. T. Y. Wong and J. W. Essex.  Isr. J. Chem. 2020, 60(7): 754.
  21. Molecular bond-breaking induced by Interatomic Coulombic Decay.  Y.-C. Chiang, S. Engin, P. Bao, F. Otto, P. Kolorenc, P. Votavova, T. Miteva, J. Gao and N. Sisourat.  Phys. Rev. A 2019, 100(5): 052701.
  22. Structural and dynamic basis of substrate permissiveness in hydroxy-cinnamoyl-transferase (HCT).  Y.-C. Chiang, O. Levsh, C. K. Lam, J.-K. Weng and Y. Wang.  PLOS Comput. Biol. 2018, 14(10): e1006511.
  23. Mechanistic basis for the evolution of chalcone synthase catalytic cysteine reactivity in land plants.  G. Liou, Y.-C. Chiang, Y. Wang and J.-K. Weng.  J. Biol. Chem. 2018, 293(48): 18601-18612.
  24. Accuracy of Potfit-based potential representations and its impact on the performance of (ML-)MCTDH.  F. Otto, Y.-C. Chiang and D. Peláez.  Chem. Phys. 2018, 509: 116-130.
  25. Strong field control of the interatomic Coulombic decay process in quantum dots.  A. Haller, Y.-C. Chiang, M. Menger, E. F. Aziz and A. Bande.  Chem. Phys. 2017, 482: 135-145.
  26. Dynamic conformational states dictate selectivity toward native substrate in a substrate-permissive acyltransferase.  O. Levsh, Y.-C. Chiang, C. F. Tung, J. P. Noel, Y. Wang and J.-K. Weng.  Biochemistry. 2016, 55(45): 6314-6326.
  27. The role of intramolecular nonbonded interaction and angle sampling in single-step free energy perturbation.  Y.-C. Chiang, Y. T. Pang and Y. Wang.  J. Chem. Phys. 2016, 145(23): 234109.
  28. Virtual substitution scan via single-step free energy perturbation.  Y.-C. Chiang and Y. Wang.  Biopolymers. 2016, 105(6): 324-336.
  29. Ab initio calculation of ICD widths in photoexcited HeNe.  G. Jabbari, S. Klaiman, Y.-C. Chiang, F. Trinter, T. Jahnke and K. Gokhberg.  J. Chem. Phys. 2014, 140(22): 224305.
  30. The effect of the partner atom on the spectra of interatomic Coulombic decay triggered by resonant Auger processes.  T. Miteva, Y.-C. Chiang, P. Kolorenc, A. I. Kuleff, L. S. Cederbaum and K. Gokhberg.  J. Chem. Phys. 2014, 141(16): 164303.
  31. Interatomic Coulombic decay following resonant core excitation of Ar in argon dimer.  T. Miteva, Y.-C. Chiang, P. Kolorenc, A. I. Kuleff, K. Gokhberg and L. S. Cederbaum.  J. Chem. Phys. 2014, 141(6): 064307.
  32. The exact wavefunction factorization of a vibronic coupling system.  Y.-C. Chiang, S. Klaiman, F. Otto and L. S. Cederbaum.  J. Chem. Phys. 2014, 140(5): 054104.
  33. Vibrationally resolved decay width of Interatomic Coulombic Decay in HeNe.  F. Trinter, J. B. Williams, M. Weller, M. Waitz, M. Pitzer, J. Voigtsberger, C. Schober, G. Kastirke, C. Müller, C. Goihl, P. Burzynski, F. Wiegandt, R. Wallauer, A. Kalinin, L. Ph. H. Schmidt, M. S. Schöffler, Y.-C. Chiang, K. Gokhberg, T. Jahnke, and R. Dörner.  Phys. Rev. Lett. 2013, 111(23): 233004.
  34. Quenching molecular photodissociation by intermolecular Coulombic decay.  S. Kopelke, Y.-C. Chiang, K. Gokhberg and L. S. Cederbaum.  J. Chem. Phys. 2012, 137(3): 034302.
  35. Kinetic energy release in fragmentation processes following electron emission: A time-dependent approach.  Y.-C. Chiang, F. Otto, H.-D. Meyer and L. S. Cederbaum.  J. Chem. Phys. 2012, 136(11): 114111.
  36. Interatomic Coulombic decay following Ne 1s Auger decay in NeAr.  T. Ouchi, K. Sakai, H. Fukuzawa, I. Higuchi, Ph. V. Demekhin, Y.-C. Chiang, S. D. Stoychev, A. I. Kuleff, T. Mazza, M. Schöffler, K. Nagaya, M. Yao, Y. Tamenori, N. Saito and K. Ueda.  Phys. Rev. A 2011, 83(5): 053415.
  37. Resonant Auger decay of the core-excited C*O molecule in intense X-ray laser fields.  Ph. V. Demekhin, Y.-C. Chiang and L. S. Cederbaum.  Phys. Rev. A 2011, 84(3): 033417.
  38. Interrelation between the distributions of kinetic energy release and emitted electron energy following the decay of electronic states.  Y.-C. Chiang, F. Otto, H.-D. Meyer and L. S. Cederbaum.  Phys. Rev. Lett. 2011, 107(17): 173001.
  39. Resonant Auger decay of molecules in intense X-ray laser fields: Light-induced strong nonadiabatic effects.  L. S. Cederbaum, Y.-C. Chiang, Ph. V. Demekhin and N. Moiseyev.  Phys. Rev. Lett. 2011, 106(12): 123001.
  40. Interatomic electronic decay processes in singly and multiply ionized clusters.  V. Averbukh, Ph. V. Demekhin, P. Kolorenč, S. Scheit, S. D. Stoychev, A. I. Kuleff, Y.-C. Chiang, K. Gokhberg, S. Kopelke, N. Sisourat, and L. S. Cederbaum.  J. Electron. Spectrosc. Relat. Phenom. 2011, 183(1-3): 36-47.
  41. Linewidth and lifetime of atomic levels and the time evolution of spectra and coincidence spectra.  Y.-C. Chiang, P. V. Demekhin, A. I. Kuleff, S. Scheit and L. S. Cederbaum.  Phys. Rev. A 2010, 81(3): 032511.
  42. Interatomic Coulombic decay and its dynamics in NeAr following K-LL Auger transition in the Ne atom.  Ph. V. Demekhin, Y.-C. Chiang, S. D. Stoychev, P. Kolorenc, S. Scheit, A. I. Kuleff, F. Tarantelli and L. S. Cederbaum.  J. Chem. Phys. 2009, 131(10): 104303.