Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set

References

• Adeniyi, A. A., & Soliman, M. E. (2017). Implementing QM in docking calculations: Is it a waste of computational time? Drug Discovery Today, 22(8), 1216–1223. https://doi.org/10.1016/j.drudis.2017.06.012
   PubMed Web of Science ®Google Scholar
• Aldeghi, M., Heifetz, A., Bodkin, M. J., Knapp, S., & Biggin, P. C. (2016). Accurate calculation of the absolute free energy of binding for drug molecules. Chemical Science, 7(1), 207–218. https://doi.org/10.1039/C5SC02678D
   PubMed Web of Science ®Google Scholar
• Allen, W. J., & Rizzo, R. C. (2014). Implementation of the Hungarian algorithm to account for ligand symmetry and similarity in structure-based design. Journal of Chemical Information and Modeling, 54(2), 518–529. https://doi.org/10.1021/ci400534h
   PubMed Web of Science ®Google Scholar
• Alvarez, J. C. (2004). High-throughput docking as a source of novel drug leads. Current Opinion in Chemical Biology, 8(4), 365–370. https://doi.org/10.1016/j.cbpa.2004.05.001
   PubMed Web of Science ®Google Scholar
• Bagheri, S., Behnejad, H., Firouzi, R., & Karimi‐Jafari, M. H. (2020). Using the semiempirical quantum mechanics in improving the molecular docking: A case study with CDK2. Molecular Informatics, 39(9), 2000036. https://doi.org/10.1002/minf.202000036
   Web of Science ®Google Scholar
• Ballante, F., & Marshall, G. R. (2016). An automated strategy for binding-pose selection and docking assessment in structure-based drug design. Journal of Chemical Information and Modeling, 56(1), 54–72. https://doi.org/10.1021/acs.jcim.5b00603
   PubMed Web of Science ®Google Scholar
• Bender, B. J., Gahbauer, S., Luttens, A., Lyu, J., Webb, C. M., Stein, R. M., Fink, E. A., Balius, T. E., Carlsson, J., Irwin, J. J., & Shoichet, B. K. (2021). A practical guide to large-scale docking. Nature Protocols, 16(10), 4799–4832. https://doi.org/10.1038/s41596-021-00597-z
   PubMed Web of Science ®Google Scholar
• Borbulevych, O. Y., Plumley, J. A., Martin, R. I., Merz, K. M., & Westerhoff, L. M. (2014). Accurate macromolecular crystallographic refinement: Incorporation of the linear scaling, semiempirical quantum-mechanics program DivCon into the PHENIX refinement package. Acta Crystallographica. Section D, Biological Crystallography, 70(Pt 5), 1233–1247. https://doi.org/10.1107/S1399004714002260
   PubMedGoogle Scholar
• Chen, G., Seukep, A. J., & Guo, M. (2020). Recent advances in molecular docking for the research and discovery of potential marine drugs. Marine Drugs, 18(11), 545. https://doi.org/10.3390/md18110545
   PubMed Web of Science ®Google Scholar
• Chen, Y. C. (2014). Beware of docking!. Trends in Pharmacological Sciences, 36(2), 78–95. https://doi.org/10.1016/j.tips.2014.12.001
   PubMed Web of Science ®Google Scholar
• Christensen, A. S., Kubař, T., Cui, Q., & Elstner, M. (2016). Semiempirical quantum mechanical methods for noncovalent interactions for chemical and biochemical applications. Chemical Reviews, 116(9), 5301–5337. https://doi.org/10.1021/acs.chemrev.5b00584
   PubMed Web of Science ®Google Scholar
• Dittrich, J., Schmidt, D., Pfleger, C., & Gohlke, H. (2019). Converging a knowledge-based scoring function: DrugScore2018. Journal of Chemical Information and Modeling, 59(1), 509–521. https://doi.org/10.1021/acs.jcim.8b00582
   PubMed Web of Science ®Google Scholar
• Dobes, P., Rezác, J., Fanfrlík, J., Otyepka, M., & Hobza, P. (2011). Semiempirical quantum mechanical method PM6-DH2X describes the geometry and energetics of CK2-inhibitor complexes involving halogen bonds well, while the empirical potential fails. The Journal of Physical Chemistry. B, 115(26), 8581–8589. https://doi.org/10.1021/jp202149z
   PubMed Web of Science ®Google Scholar
• Fine, J., Konc, J., Samudrala, R., & Chopra, G. (2020). CANDOCK: Chemical atomic network-based hierarchical flexible docking algorithm using generalized statistical potentials. Journal of Chemical Information and Modeling, 60(3), 1509–1527. https://doi.org/10.1021/acs.jcim.9b00686
   PubMed Web of Science ®Google Scholar
• Firouzi, R., & Ashouri, M. (2023). Identification of potential anti‐COVID‐19 drug leads from medicinal plants through virtual high‐throughput screening. ChemistrySelect, 8(7), e202203865. https://doi.org/10.1002/slct.202203865
   Web of Science ®Google Scholar
• Firouzi, R., & Shahbazian, S. (2014). Seeking for ultrashort “non-bonded” hydrogen–hydrogen contacts in some rigid hydrocarbons and their chlorinated derivatives. Structural Chemistry, 25(4), 1297–1304. https://doi.org/10.1007/s11224-014-0411-9
   Web of Science ®Google Scholar
• Firouzi, R., & Shahbazian, S. (2016). Seeking extremes in molecular design: To what extent may two “Non‐Bonded” Hydrogen atoms be squeezed in a hydrocarbon? Chemphyschem, 17(1), 51–54. https://doi.org/10.1002/cphc.201501002
   PubMed Web of Science ®Google Scholar
• Firouzi, R., Ashouri, M., & Karimi‐Jafari, M. H. (2022). Structural insights into the substrate‐binding site of main protease for the structure‐based COVID‐19 drug discovery. Proteins, 90(5), 1090–1101. https://doi.org/10.1002/prot.26318
   PubMed Web of Science ®Google Scholar
• Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S., & Olson, A. J. (2016). Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nature Protocols, 11(5), 905–919. https://doi.org/10.1038/nprot.2016.051
   PubMed Web of Science ®Google Scholar
• Fukunishi, Y., Higo, J., & Kasahara, K. (2022). Computer simulation of molecular recognition in biomolecular system: From in silico screening to generalized ensembles. Biophysical Reviews, 14(6), 1423–1447. https://pubmed.ncbi.nlm.nih.gov/36465086. https://doi.org/10.1007/s12551-022-01015-8
   PubMedGoogle Scholar
• Gardiner, E. J., Willett, P., & Artymiuk, P. J. (2001). Protein docking using a genetic algorithm. Proteins, 44(1), 44–56. https://doi.org/10.1002/prot.1070
   PubMed Web of Science ®Google Scholar
• Gasteiger, J., & Marsili, M. (1978). A new model for calculating atomic charges in molecules. Tetrahedron Letters. 19(34), 3181–3184. https://doi.org/10.1016/s0040-4039(01)94977-9
   Google Scholar
• Gasteiger, J., & Marsili, M. (1980). Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron, 36(22), 3219–3228. https://doi.org/10.1016/0040-4020(80)80168-2
   Web of Science ®Google Scholar
• Gentile, F., Oprea, T. I., Tropsha, A., & Cherkasov, A. (2023). Surely you are joking, Mr Docking!. Chemical Society Reviews, 52(3), 872–878. https://doi.org/10.1039/D2CS00948J
   PubMed Web of Science ®Google Scholar
• Golebiowski, A., Klopfenstein, S. R., & Portlock, D. E. (2003). Lead compounds discovered from libraries: Part 2. Current Opinion in Chemical Biology, 7(3), 308–325. https://doi.org/10.1016/s1367-5931(03)00059-0
   PubMed Web of Science ®Google Scholar
• Goodford, P. J. (1985). A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. Journal of Medicinal Chemistry, 28(7), 849–857. https://doi.org/10.1021/jm00145a002
   PubMed Web of Science ®Google Scholar
• Gorgulla, C., Boeszoermenyi, A., Wang, Z.-F., Fischer, P. D., Coote, P. W., Padmanabha Das, K. M., Malets, Y. S., Radchenko, D. S., Moroz, Y. S., Scott, D. A., Fackeldey, K., Hoffmann, M., Iavniuk, I., Wagner, G., & Arthanari, H. (2020). An open-source drug discovery platform enables ultra-large virtual screens. Nature, 580(7805), 663–668. https://doi.org/10.1038/s41586-020-2117-z
   PubMed Web of Science ®Google Scholar
• Guedes, I. A., Barreto, A. M., Marinho, D., Krempser, E., Kuenemann, M. A., Sperandio, O., Dardenne, L. E., & Miteva, M. A. (2021). New machine learning and physics-based scoring functions for drug discovery. Scientific Reports, 11(1), 3198. https://doi.org/10.1038/s41598-021-82410-1
   PubMedGoogle Scholar
• Guedes, I. A., de Magalhães, C. S., & Dardenne, L. E. (2014). Receptor–ligand molecular docking. Biophysical Reviews, 6(1), 75–87. https://doi.org/10.1007/s12551-013-0130-2
   PubMedGoogle Scholar
• Halperin, I., Ma, B., Wolfson, H., & Nussinov, R. (2002). Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins, 47(4), 409–443. https://doi.org/10.1002/prot.10115
   PubMed Web of Science ®Google Scholar
• Hostaš, J., Řezáč, J., & Hobza, P. (2013). On the performance of the semiempirical quantum mechanical PM6 and PM7 methods for noncovalent interactions. Chemical Physics Letters. 568–569, 161–166. https://doi.org/10.1016/j.cplett.2013.02.069
   Web of Science ®Google Scholar
• Huang, S. Y., & Zou, X. (2010). Advances and challenges in protein-ligand docking. International Journal of Molecular Sciences, 11(8), 3016–3034. https://doi.org/10.3390/ijms11083016
   PubMed Web of Science ®Google Scholar
• Ilatovskiy, A. V., Abagyan, R., & Kufareva, I. (2013). Quantum mechanics approaches to drug research in the era of structural chemogenomics. International Journal of Quantum Chemistry, 113(12), 1669–1675. https://doi.org/10.1002/qua.24400
   PubMed Web of Science ®Google Scholar
• Jorgensen, W. L. (2004). The many roles of computation in drug discovery. Science (New York, N.Y.), 303(5665), 1813–1818. https://doi.org/10.1126/science.1096361
   PubMed Web of Science ®Google Scholar
• Kar, S., & Roy, K. (2013). How far can virtual screening take us in drug discovery? Expert Opinion on Drug Discovery, 8(3), 245–261. https://doi.org/10.1517/17460441.2013.761204
   PubMed Web of Science ®Google Scholar
• Kitchen, D. B., Decornez, H., Furr, J. R., & Bajorath, J. (2004). Docking and scoring in virtual screening for drug discovery: Methods and applications. Nature Reviews. Drug Discovery, 3(11), 935–949. https://doi.org/10.1038/nrd1549
   PubMed Web of Science ®Google Scholar
• Kriz, K., & Rezac, J. (2019). Reparametrization of the COSMO solvent model for semiempirical methods PM6 and PM7. Journal of Chemical Information and Modeling. 59, 229–235. https://doi.org/10.1021/acs.jcim.8b00681
   PubMed Web of Science ®Google Scholar
• Kříž, K., & Řezáč, J. (2020). Benchmarking of semiempirical quantum-mechanical methods on systems relevant to computer-aided drug design. Journal of Chemical Information and Modeling, 60(3), 1453–1460. https://doi.org/10.1021/acs.jcim.9b01171
   PubMed Web of Science ®Google Scholar
• Lavecchia, A., & Di Giovanni, C. (2013). Virtual screening strategies in drug discovery: A critical review. Current Medicinal Chemistry, 20(23), 2839–2860. https://doi.org/10.2174/09298673113209990001
   PubMed Web of Science ®Google Scholar
• Lepšík, M., Řezáč, J., Kolář, M., Pecina, A., Hobza, P., & Fanfrlík, J. (2013). The semiempirical quantum mechanical scoring function for in silico drug design. ChemPlusChem, 78(9), 921–931. https://doi.org/10.1002/cplu.201300199
   PubMed Web of Science ®Google Scholar
• Li, H., Sze, K. H., Lu, G., & Ballester, P. J. (2021). Machine‐learning scoring functions for structure‐based drug lead optimization. Wiley Interdiscip. Rev. Comput. Mol. Sci, 11, e1478. https://doi.org/10.1002/wcms.1478
   Web of Science ®Google Scholar
• Marrink, S. J., Monticelli, L., Melo, M. N., Alessandri, R., Tieleman, D. P., & Souza, P. C. (2023). Two decades of Martini: Better beads, broader scope. WIREs Computational Molecular Science, 13(1), e1620. https://doi.org/10.1002/wcms.1620
   Google Scholar
• Mcgann, M. R., Almond, H. R., Nicholls, A., Grant, J. A., & Brown, F. K. (2003). Gaussian docking functions. Biopolymers, 68(1), 76–90. https://doi.org/10.1002/bip.10207
   PubMed Web of Science ®Google Scholar
• McNutt, A. T., Francoeur, P., Aggarwal, R., Masuda, T., Meli, R., Ragoza, M., Sunseri, J., & Koes, D. R. (2021). GNINA 1.0: Molecular docking with deep learning. Journal of Cheminformatics, 13(1), 43. https://doi.org/10.1186/s13321-021-00522-2
   PubMed Web of Science ®Google Scholar
• Meng, X. Y., Zhang, H. X., Mezei, M., & Cui, M. (2011). Molecular docking: A powerful approach for structure-based drug discovery. Current Computer-Aided Drug Design, 7(2), 146–157. https://doi.org/10.2174/157340911795677602
   PubMed Web of Science ®Google Scholar
• Merz, K. M. (2014). Using quantum mechanical approaches to study biological systems. Accounts of Chemical Research, 47(9), 2804–2811. https://doi.org/10.1021/ar5001023
   PubMed Web of Science ®Google Scholar
• Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/jcc.21256
   PubMed Web of Science ®Google Scholar
• Murugan, N. A., Podobas, A., Gadioli, D., Vitali, E., Palermo, G., & Markidis, S. (2022). A review on parallel virtual screening softwares for high-performance computers. Pharmaceuticals, 15(1), 63. https://doi.org/10.3390/ph15010063
   Web of Science ®Google Scholar
• Nekardová, M., Vymětalová, L., Khirsariya, P., Kováčová, S., Hylsová, M., Jorda, R., Kryštof, V., Fanfrlík, J., Hobza, P., & Paruch, K. (2017). Structural basis of the interaction of cyclin‐dependent kinase 2 with roscovitine and its analogues having bioisosteric central heterocycles. Chemphyschem: A European Journal of Chemical Physics and Physical Chemistry, 18(7), 785–795. https://doi.org/10.1002/cphc.201601319
   PubMed Web of Science ®Google Scholar
• Pan, Y. K. (1971). Approximate molecular orbital theory (Pople, John A.; Beveridge, David L.). Journal of Chemical Education, 48(2), A116. https://doi.org/10.1021/ed048pA116.1
   Google Scholar
• Peng, C., Wang, J., Yu, Y., Wang, G., Chen, Z., Xu, Z., Cai, T., Shao, Q., Shi, J., & Zhu, W. (2019). Improving the accuracy of predicting protein–ligand binding-free energy with semiempirical quantum chemistry charge. Future Medicinal Chemistry, 11(4), 303–321. https://doi.org/10.4155/fmc-2018-0207
   PubMed Web of Science ®Google Scholar
• Raha, K., Peters, M. B., Wang, B., Yu, N., Wollacott, A. M., Westerhoff, L. M., & Merz, K. M. Jr (2007). The role of quantum mechanics in structure-based drug design. Drug Discovery Today, 12(17-18), 725–731. https://doi.org/10.1016/j.drudis.2007.07.006
   PubMed Web of Science ®Google Scholar
• Rayka, M., Karimi‐Jafari, M. H., & Firouzi, R. (2021). ET‐score: Improving Protein‐ligand Binding Affinity Prediction Based on Distance‐weighted Interatomic Contact Features Using Extremely Randomized Trees Algorithm. Molecular Informatics, 40(8), 2060084. https://doi.org/10.1002/minf.202060084
   Web of Science ®Google Scholar
• Read, R. J., Hart, T. N., Cummings, M. D., & Ness, S. R. (1995). Monte Carlo algorithms for docking to proteins. Supramolecular Chemistry. 6(1–2), 135–140. https://doi.org/10.1080/10610279508032529
   Web of Science ®Google Scholar
• Řezáč, J., & Stewart, J. J. (2023). How well do semiempirical QM methods describe the structure of proteins? The Journal of Chemical Physics, 158(4), 044118. https://doi.org/10.1063/5.0135091
   PubMed Web of Science ®Google Scholar
• Ryde, U., & Söderhjelm, P. (2016). Ligand-binding affinity estimates supported by quantum-mechanical methods. Chemical Reviews, 116(9), 5520–5566. https://doi.org/10.1021/acs.chemrev.5b00630
   PubMed Web of Science ®Google Scholar
• Sadybekov, A. V., & Katritch, V. (2023). Computational approaches streamlining drug discovery. Nature, 616(7958), 673–685. https://doi.org/10.1038/s41586-023-05905-z
   PubMed Web of Science ®Google Scholar
• Schneider, G. (2010). Virtual screening: An endless staircase? Nature Reviews. Drug Discovery, 9(4), 273–276. https://doi.org/10.1038/nrd3139
   PubMed Web of Science ®Google Scholar
• Shen, C., Ding, J., Wang, Z., Cao, D., Ding, X., & Hou, T. (2019). From machine learning to deep learning: Advances in scoring functions for protein–ligand docking. WIREs Computational Molecular Science, 10(1), e1429. https://doi.org/10.1002/wcms.1429
   Google Scholar
• Shen, C., Wang, Z., Yao, X., Li, Y., Lei, T., Wang, E., Xu, L., Zhu, F., Li, D., & Hou, T. (2020). Comprehensive assessment of nine docking programs on type II kinase inhibitors: Prediction accuracy of sampling power, scoring power and screening power. Briefings in Bioinformatics, 21(1), 282–297. https://doi.org/10.1093/bib/bby103
   PubMed Web of Science ®Google Scholar
• Stewart, J. J. (2013). Optimization of parameters for semiempirical methods VI: More modifications to the NDDO approximations and re-optimization of parameters. Journal of Molecular Modeling, 19(1), 1–32. https://doi.org/10.1007/s00894-012-1667-x
   PubMed Web of Science ®Google Scholar
• Stewart, J. J. P. (2016). Stewart computational chemistry. MOPAC.
   Google Scholar
• Stigliani, J. L., Bernardes-Génisson, V., Bernadou, J., & Pratviel, G. (2012). Cross-docking study on InhA inhibitors: A combination of Autodock Vina and PM6-DH2 simulations to retrieve bio-active conformations. Organic & Biomolecular Chemistry, 10(31), 6341–6349. https://doi.org/10.1039/c2ob25602a
   PubMed Web of Science ®Google Scholar
• Su, M., Yang, Q., Du, Y., Feng, G., Liu, Z., Li, Y., & Wang, R. (2019). Comparative assessment of scoring functions: The CASF-2016 update. Journal of Chemical Information and Modeling, 59(2), 895–913. https://doi.org/10.1021/acs.jcim.8b00545
   PubMed Web of Science ®Google Scholar
• Sulimov, A. V., Kutov, D. C., Katkova, E. V., & Sulimov, V. B. (2017). Combined docking with classical force field and quantum chemical semiempirical method PM7. Advances in Bioinformatics, 2017, 7167691–7167696. https://doi.org/10.1155/2017/7167691
   PubMedGoogle Scholar
• Thiel, W. (2014). Semiempirical quantum–chemical methods. Wiley Interdiscip. WIREs Computational Molecular Science, 4(2), 145–157. https://doi.org/10.1002/wcms.1161
   Google Scholar
• Torres, P. H., Sodero, A. C., Jofily, P., & Silva, F. P. (2019). Key topics in molecular docking for drug design. International Journal of Molecular Sciences, 20(18), 4574. https://doi.org/10.3390/ijms20184574
   PubMed Web of Science ®Google Scholar
• Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334
   PubMed Web of Science ®Google Scholar
• Wang, A., Zhang, Y., Chu, H., Liao, C., Zhang, Z., & Li, G. (2020). Higher accuracy achieved for protein–ligand binding pose prediction by elastic network model-based ensemble docking. Journal of Chemical Information and Modeling, 60(6), 2939–2950. https://doi.org/10.1021/acs.jcim.9b01168
   PubMed Web of Science ®Google Scholar
• Wang, J. C., Lin, J. H., Chen, C. M., Perryman, A. L., & Olson, A. J. (2011). Robust scoring functions for protein–ligand interactions with quantum chemical charge models. Journal of Chemical Information and Modeling, 51(10), 2528–2537. https://doi.org/10.1021/ci200220v
   PubMed Web of Science ®Google Scholar
• Wang, R., Lu, Y., & Wang, S. (2003). Comparative evaluation of 11 scoring functions for molecular docking. Journal of Medicinal Chemistry, 46(12), 2287–2303. https://doi.org/10.1021/jm0203783
   PubMed Web of Science ®Google Scholar
• Wang, Z., Sun, H., Yao, X., Li, D., Xu, L., Li, Y., Tian, S., & Hou, T. (2016). Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: The prediction accuracy of sampling power and scoring power. Physical Chemistry Chemical Physics, 18(18), 12964–12975. https://doi.org/10.1039/C6CP01555G
   PubMed Web of Science ®Google Scholar
• Webber, W., Moffat, A., & Zobel, J. (2010). A similarity measure for indefinite rankings. ACM Transactions on Information Systems, 28(4), 1–38. https://doi.org/10.1145/1852102.1852106
   Web of Science ®Google Scholar
• Wei, L., Chi, B., Ren, Y., Rao, L., Wu, J., Shang, H., Liu, J., Xiao, Y., Ma, M., Xu, X., & Wan, J. (2019). Conformation search across multiple-level potential-energy surfaces (CSAMP): A strategy for accurate prediction of protein–ligand binding structures. Journal of Chemical Theory and Computation, 15(7), 4264–4279. https://doi.org/10.1021/acs.jctc.8b01150
   PubMed Web of Science ®Google Scholar
• Willow, S. Y., Xie, B., Lawrence, J., Eisenberg, R. S., & Minh, D. D. (2020). On the polarization of ligands by proteins. Physical Chemistry Chemical Physics, 22(21), 12044–12057. https://doi.org/10.1039/D0CP00376J
   PubMed Web of Science ®Google Scholar
• Word, J. M., Lovell, S. C., Richardson, J. S., & Richardson, D. C. (1999). Asparagine and glutamine: Using hydrogen atom contacts in the choice of side-chain amide orientation. Journal of Molecular Biology, 285(4), 1735–1747. https://doi.org/10.1006/jmbi.1998.2401
   PubMed Web of Science ®Google Scholar
• Xia, J., Tilahun, E. L., Reid, T. E., Zhang, L., & Wang, X. S. (2015). Benchmarking methods and data sets for ligand enrichment assessment in virtual screening. Methods, 71, 146–157. https://doi.org/10.1016/j.ymeth.2014.11.015
   PubMed Web of Science ®Google Scholar
• Xu, M., Shen, C., Yang, J., Wang, Q., & Huang, N. (2022). Systematic investigation of docking failures in large-scale structure-based virtual screening. ACS Omega, 7(43), 39417–39428. https://doi.org/10.1021/acsomega.2c05826
   PubMed Web of Science ®Google Scholar
• Zhang, B., Li, H., Yu, K., & Jin, Z. (2022). Molecular docking-based computational platform for high-throughput virtual screening. CCF Transactions on High Performance Computing, 4(1), 63–74. https://doi.org/10.1007/s42514-021-00086-5
   PubMedGoogle Scholar
• Zhou, T., Huang, D., & Caflisch, A. (2008). Is quantum mechanics necessary for predicting binding free energy? Journal of Medicinal Chemistry, 51(14), 4280–4288. https://doi.org/10.1021/jm800242q
   PubMed Web of Science ®Google Scholar
• Zhou, T., Huang, D., & Caflisch, A. (2010). Quantum mechanical methods for drug design. Current Topics in Medicinal Chemistry, 10(1), 33–45. https://doi.org/10.2174/156802610790232242
   PubMed Web of Science ®Google Scholar