ACS Publications. Most Trusted. Most Cited. Most Read
QM/MM Calculations in Drug Discovery: A Useful Method for Studying Binding Phenomena?
My Activity

Figure 1Loading Img
    Article

    QM/MM Calculations in Drug Discovery: A Useful Method for Studying Binding Phenomena?
    Click to copy article linkArticle link copied!

    View Author Information
    Computational & Structural Chemistry, GlaxoSmithKline Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, United Kingdom, and Department of Chemistry, Faculty of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
    * Corresponding author phone: +44 (0)1438 768682; fax: +44 (0) 1438 763352; e-mail: [email protected]
    †GlaxoSmithKline Medicines Research Centre.
    ‡King Mongkut’s Institute of Technology Ladkrabang.
    Other Access Options

    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2009, 49, 3, 670–677
    Click to copy citationCitation copied!
    https://doi.org/10.1021/ci800419j
    Published February 13, 2009
    Copyright © 2009 American Chemical Society

    Abstract

    Click to copy section linkSection link copied!
    Abstract Image

    Herein we investigate whether QM/MM could prove useful as a tool to study the often subtle binding phenomena found within pharmaceutical drug discovery programs. The goal of this investigation is to determine whether it is possible to employ high level QM/MM calculations to answer specific questions around a binding event in a cycle time that is aligned with medicinal chemistry synthesis. To this end QM/MM calculations have been performed on four protein kinase-ligand complexes using five different levels of theory, using standard hardware, in an effort to assess their utility. We conclude that the accuracy and turnaround time of such calculations mean they could prove valuable to (1) probe the subtle nature of the interactions within protein active sites, (2) facilitate the interpretation of poorly resolved electron density, and (3) study the impact of substituent changes on the binding conformation or in the assessment of alternate scaffolds. In practice, the successful application of such methods will be limited by the size of the system under investigation, the level of theory used, and whether there is a need for conformational sampling.

    Copyright © 2009 American Chemical Society

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.

    Recommended

    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. Add or change your institution or let them know you’d like them to include access.

    Cited By

    Click to copy section linkSection link copied!

    This article is cited by 71 publications.

    1. Juan S. Grassano, Ignacio Pickering, Adrian E. Roitberg, Mariano C. González Lebrero, Dario A. Estrin, Jonathan A. Semelak. Assessment of Embedding Schemes in a Hybrid Machine Learning/Classical Potentials (ML/MM) Approach. Journal of Chemical Information and Modeling 2024, 64 (10) , 4047-4058. https://doi.org/10.1021/acs.jcim.4c00478
    2. Kalyanashis Jana, Shibaji Ghosh, Padmaja D. Wakchaure, Tusar Bandyopadhyay, Bishwajit Ganguly. Probing the Role of Imidazopyridine and Imidazophosphorine Scaffolds To Design Novel Proton Pump Inhibitor for H+,K+-ATPase: A DFT Study. ACS Omega 2019, 4 (1) , 1311-1321. https://doi.org/10.1021/acsomega.8b02756
    3. Ulf Ryde and Pär Söderhjelm . Ligand-Binding Affinity Estimates Supported by Quantum-Mechanical Methods. Chemical Reviews 2016, 116 (9) , 5520-5566. https://doi.org/10.1021/acs.chemrev.5b00630
    4. Maria G. Khrenova, Bella L. Grigorenko, Anatoly B. Kolomeisky, and Alexander V. Nemukhin . Hydrolysis of Guanosine Triphosphate (GTP) by the Ras·GAP Protein Complex: Reaction Mechanism and Kinetic Scheme. The Journal of Physical Chemistry B 2015, 119 (40) , 12838-12845. https://doi.org/10.1021/acs.jpcb.5b07238
    5. Lung Wa Chung, W. M. C. Sameera, Romain Ramozzi, Alister J. Page, Miho Hatanaka, Galina P. Petrova, Travis V. Harris, Xin Li, Zhuofeng Ke, Fengyi Liu, Hai-Bei Li, Lina Ding, and Keiji Morokuma . The ONIOM Method and Its Applications. Chemical Reviews 2015, 115 (12) , 5678-5796. https://doi.org/10.1021/cr5004419
    6. Marc W. van der Kamp and Adrian J. Mulholland . Combined Quantum Mechanics/Molecular Mechanics (QM/MM) Methods in Computational Enzymology. Biochemistry 2013, 52 (16) , 2708-2728. https://doi.org/10.1021/bi400215w
    7. Jens Antony, Stefan Grimme, Dimitrios G. Liakos, and Frank Neese . Protein–Ligand Interaction Energies with Dispersion Corrected Density Functional Theory and High-Level Wave Function Based Methods. The Journal of Physical Chemistry A 2011, 115 (41) , 11210-11220. https://doi.org/10.1021/jp203963f
    8. Petr Dobeš, Jan Řezáč, Jindřich Fanfrlík, Michal Otyepka, and Pavel Hobza . 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 2011, 115 (26) , 8581-8589. https://doi.org/10.1021/jp202149z
    9. Seth A. Hayik, Roland Dunbrack, Jr., and Kenneth M. Merz, Jr.. Mixed Quantum Mechanics/Molecular Mechanics Scoring Function To Predict Protein−Ligand Binding Affinity. Journal of Chemical Theory and Computation 2010, 6 (10) , 3079-3091. https://doi.org/10.1021/ct100315g
    10. Yunxiang Lu, Yong Wang, Zhijian Xu, Xiuhua Yan, Xiaoming Luo, Hualiang Jiang and Weiliang Zhu. C−X···H Contacts in Biomolecular Systems: How They Contribute to Protein−Ligand Binding Affinity. The Journal of Physical Chemistry B 2009, 113 (37) , 12615-12621. https://doi.org/10.1021/jp906352e
    11. Anjali Awasthi, Amita Singh Bhal, Anamika Singh, Puneet Kumar Pandey, Monika Gupta. Ultrasonic, DFT and FTIR studies to investigate molecular interactions in binary mixtures of 2-ethoxyethanol with 2-pyrrolidinone and N-methylacetamide at different temperatures. Physics and Chemistry of Liquids 2025, 5 , 1-20. https://doi.org/10.1080/00319104.2025.2450302
    12. Panthip Tue-ngeun, Waleepan Rakitikul, Natechanok Thinkumrob, Supa Hannongbua, Wijitra Meelua, Jitrayut Jitonnom. Binding interactions and in silico ADME prediction of isoconessimine derivatives as potent acetylcholinesterase inhibitors. Journal of Molecular Graphics and Modelling 2024, 129 , 108746. https://doi.org/10.1016/j.jmgm.2024.108746
    13. Ahmed M. El-Saghier, Souhaila S. Enaili, Aly Abdou, Asmaa M. Kadry. An efficient eco-friendly, simple, and green synthesis of some new spiro-N-(4-sulfamoyl-phenyl)-1,3,4-thiadiazole-2-carboxamide derivatives as potential inhibitors of SARS-CoV-2 proteases: drug-likeness, pharmacophore, molecular docking, and DFT exploration. Molecular Diversity 2024, 28 (1) , 249-270. https://doi.org/10.1007/s11030-023-10761-0
    14. Umesh Panwar, Aarthy Murali, Mohammad Aqueel Khan, Chandrabose Selvaraj, Sanjeev Kumar Singh. Virtual Screening Process: A Guide in Modern Drug Designing. 2024, 21-31. https://doi.org/10.1007/978-1-0716-3441-7_2
    15. K. Jagadesh Babu, Dasari Ayodhya, Shivaraj. Comprehensive investigation of Co(II), Ni(II) and Cu(II) complexes derived from a novel Schiff base: Synthesis, characterization, DNA interactions, ADME profiling, molecular docking, and in-vitro biological evaluation. Results in Chemistry 2023, 6 , 101110. https://doi.org/10.1016/j.rechem.2023.101110
    16. Andrea Mastropietro, Giuseppe Pasculli, Jürgen Bajorath. Learning characteristics of graph neural networks predicting protein–ligand affinities. Nature Machine Intelligence 2023, 5 (12) , 1427-1436. https://doi.org/10.1038/s42256-023-00756-9
    17. Anamika Singh, Nikita Tiwari, Anil Mishra, Monika Gupta. DFT study and docking of xanthone derivatives indicating their ability to inhibit aromatase, a crucial enzyme for the steroid biosynthesis pathway. Computational Toxicology 2023, 28 , 100289. https://doi.org/10.1016/j.comtox.2023.100289
    18. Sudipto Kundu, Swathi N, Durai Ananda Kumar T. Discovery of pharmacological agents for triple-negative breast cancer (TNBC): molecular docking and molecular dynamic simulation studies on 5-lipoxygenase (5-LOX) and nuclear factor kappa B (NF-κB). Journal of Biomolecular Structure and Dynamics 2023, , 1-14. https://doi.org/10.1080/07391102.2023.2250449
    19. Tiago Janela, Jürgen Bajorath. Large-Scale Predictions of Compound Potency with Original and Modified Activity Classes Reveal General Prediction Characteristics and Intrinsic Limitations of Conventional Benchmarking Calculations. Pharmaceuticals 2023, 16 (4) , 530. https://doi.org/10.3390/ph16040530
    20. Tiago Janela, Kosuke Takeuchi, Jürgen Bajorath. Predicting Potent Compounds Using a Conditional Variational Autoencoder Based upon a New Structure–Potency Fingerprint. Biomolecules 2023, 13 (2) , 393. https://doi.org/10.3390/biom13020393
    21. David C. Thompson, Juan I. Rodríguez. An introduction to quantum chemistry. 2023, 21-36. https://doi.org/10.1016/B978-0-323-90891-7.00012-8
    22. Tamanna Sultana, Jarin Tasnim, Md Walid Hossain Talukder, Mohammad Liton Mia, Shamsun Nahar Suchana, Fatema Akter, Md Abu Saleh, Mst Farhana Afrin, Monir Uzzaman. Physicochemical and toxicological studies of some commonly used triazine-based herbicides; In-silico approach. Informatics in Medicine Unlocked 2023, 42 , 101378. https://doi.org/10.1016/j.imu.2023.101378
    23. Yiqun Chang, Bryson A. Hawkins, Jonathan J. Du, Paul W. Groundwater, David E. Hibbs, Felcia Lai. A Guide to In Silico Drug Design. Pharmaceutics 2023, 15 (1) , 49. https://doi.org/10.3390/pharmaceutics15010049
    24. Tiago Janela, Jürgen Bajorath. Simple nearest-neighbour analysis meets the accuracy of compound potency predictions using complex machine learning models. Nature Machine Intelligence 2022, 4 (12) , 1246-1255. https://doi.org/10.1038/s42256-022-00581-6
    25. Yue Zhang, Mengqi Luo, Peng Wu, Song Wu, Tzong-Yi Lee, Chen Bai. Application of Computational Biology and Artificial Intelligence in Drug Design. International Journal of Molecular Sciences 2022, 23 (21) , 13568. https://doi.org/10.3390/ijms232113568
    26. Md. Abdul Momin, Meherun Nesa, Monir Uzzaman, Abhijit Majumdar, A. H. Bhuiyan. Molecular dynamics, transport property, and surface stoichiometry of plasma polymerized cyclohexane thin films. AIP Advances 2022, 12 (9) https://doi.org/10.1063/5.0091730
    27. Nikita Tiwari, Ashutosh Kumar, Anjali Pandey, Anil Mishra. Computational investigation of dioxin-like compounds as human sex hormone-binding globulin inhibitors: DFT calculations, docking study and molecular dynamics simulations. Computational Toxicology 2022, 21 , 100198. https://doi.org/10.1016/j.comtox.2021.100198
    28. Mohammad Nasir Uddin, Sayeda Samina Ahmed, Monir Uzzaman, Md. Nazmul Hassan Knock, Wahhida Shumi, Abul Fazal Md. Sanaullah, Md. Mosharef Hossain Bhuyain. Characterization, molecular modeling and pharmacology of some 2́-hydroxychalcone derivatives as SARS-CoV-2 inhibitor. Results in Chemistry 2022, 4 , 100329. https://doi.org/10.1016/j.rechem.2022.100329
    29. Monir Uzzaman, Amrin Ahsan, Mohammad Nasir Uddin. Comparative assessment of some benzodiazepine drugs based on Density Functional Theory, molecular docking, and ADMET studies. European Journal of Chemistry 2021, 12 (4) , 412-418. https://doi.org/10.5155/eurjchem.12.4.412-418.2135
    30. Nikita Tiwari, Anjali Pandey, Ashutosh Kumar, Anil Mishra. Computational models reveal the potential of polycyclic aromatic hydrocarbons to inhibit aromatase, an important enzyme of the steroid biosynthesis pathway. Computational Toxicology 2021, 19 , 100176. https://doi.org/10.1016/j.comtox.2021.100176
    31. Saif Khan, Pallavi Somvanshi, Aditi Singh, Mahvish Khan, Raju K. Mandal, Sajad A. Dar, Mohd Wahid, Arshad Jawed, Bhartendu Nath Mishra, Shafiul Haque. Potency of inhibitors depends upon the accessibility of their aromatic rings within the hydrophobic specificity pocket: a novel avenue for future aldose reductase inhibitor design. Journal of Biomolecular Structure and Dynamics 2021, 39 (4) , 1512-1518. https://doi.org/10.1080/07391102.2020.1733090
    32. Hazza A. Alhobeira, Mohammed Al Mogbel, Saif Khan, Mahvish Khan, Shafiul Haque, Pallavi Somvanshi, Mohd Wahid, Raju K. Mandal. Prioritization and characterization of validated biofilm blockers targeting glucosyltransferase C of Streptococcus mutans. Artificial Cells, Nanomedicine, and Biotechnology 2021, 49 (1) , 335-344. https://doi.org/10.1080/21691401.2021.1903021
    33. Monir Uzzaman, Tareq Mahmud. Structural modification of aspirin to design a new potential cyclooxygenase (COX-2) inhibitors. In Silico Pharmacology 2020, 8 (1) https://doi.org/10.1007/s40203-020-0053-0
    34. Mohammed M. Matin, Md. S. Hasan, Monir Uzzaman, Md. Mosharef H. Bhuiyan, Sayed M. Kibria, Md. E. Hossain, Mohammad H.O. Roshid. Synthesis, spectroscopic characterization, molecular docking, and ADMET studies of mannopyranoside esters as antimicrobial agents. Journal of Molecular Structure 2020, 1222 , 128821. https://doi.org/10.1016/j.molstruc.2020.128821
    35. Parichehr Hassanzadeh. Towards the quantum-enabled technologies for development of drugs or delivery systems. Journal of Controlled Release 2020, 324 , 260-279. https://doi.org/10.1016/j.jconrel.2020.04.050
    36. Jinfeng Liu, Xiao He. Fragment-based quantum mechanical approach to biomolecules, molecular clusters, molecular crystals and liquids. Physical Chemistry Chemical Physics 2020, 22 (22) , 12341-12367. https://doi.org/10.1039/D0CP01095B
    37. Monir Uzzaman, Md. Junaid, Mohammad Nasir Uddin. Evaluation of anti-tuberculosis activity of some oxotitanium(IV) Schiff base complexes; molecular docking, dynamics simulation and ADMET studies. SN Applied Sciences 2020, 2 (5) https://doi.org/10.1007/s42452-020-2644-0
    38. Mohammad Nasir Uddin, Md Nazmul Hassan Knock, Monir Uzzaman, M. Mosharef H. Bhuiyan, A.F.M. Sanaullah, Wahhida Shumi, Hossain Md Sadrul Amin. Microwave assisted synthesis, characterization, molecular docking and pharmacological activities of some new 2′-hydroxychalcone derivatives. Journal of Molecular Structure 2020, 1206 , 127678. https://doi.org/10.1016/j.molstruc.2020.127678
    39. Shae-Lynn J. Lahey, Christopher N. Rowley. Simulating protein–ligand binding with neural network potentials. Chemical Science 2020, 11 (9) , 2362-2368. https://doi.org/10.1039/C9SC06017K
    40. Vitor Won-Held Rabelo, Izabel Christina Nunes de Palmer Paixão, Paula Alvarez Abreu. Targeting Chikungunya virus by computational approaches: from viral biology to the development of therapeutic strategies. Expert Opinion on Therapeutic Targets 2020, 24 (1) , 63-78. https://doi.org/10.1080/14728222.2020.1712362
    41. Jinfeng Liu, Xiao He. QM Implementation in Drug Design: Does It Really Help?. 2020, 19-35. https://doi.org/10.1007/978-1-0716-0282-9_2
    42. Monir Uzzaman, Jakaria Shawon, Zainul Abedin Siddique. Molecular docking, dynamics simulation and ADMET prediction of Acetaminophen and its modified derivatives based on quantum calculations. SN Applied Sciences 2019, 1 (11) https://doi.org/10.1007/s42452-019-1442-z
    43. Saw Simeon, Nathjanan Jongkon, Warot Chotpatiwetchkul, M. Paul Gleeson. Insights into the EGFR SAR of N-phenylquinazolin-4-amine-derivatives using quantum mechanical pairwise-interaction energies. Journal of Computer-Aided Molecular Design 2019, 33 (8) , 745-757. https://doi.org/10.1007/s10822-019-00221-z
    44. Monir Uzzaman, Mohammad Nasir Uddin. Optimization of structures, biochemical properties of ketorolac and its degradation products based on computational studies. DARU Journal of Pharmaceutical Sciences 2019, 27 (1) , 71-82. https://doi.org/10.1007/s40199-019-00243-w
    45. Jakaria Shawon, Akib Mahmud Khan, Adhip Rahman, Mohammad Mazharol Hoque, Mohammad Abdul Kader Khan, Mohammed G. Sarwar, Mohammad A. Halim. Molecular Recognition of Azelaic Acid and Related Molecules with DNA Polymerase I Investigated by Molecular Modeling Calculations. Interdisciplinary Sciences: Computational Life Sciences 2018, 10 (3) , 525-537. https://doi.org/10.1007/s12539-016-0186-3
    46. Zhenfan Wang, Minjun Jiang, Ninghan Feng, Chen Li. Fishing wild-type sparing inhibitors of proto-oncogene c-met variants in renal cell carcinoma from a curated tyrosine kinase inhibitor pool using analog-sensitive kinase technology. Biochimie 2018, 152 , 188-197. https://doi.org/10.1016/j.biochi.2018.07.005
    47. Mohammad Nasir Uddin, Sonia Khandaker, Moniruzzaman, Md. Shaharier Amin, Wahhida Shumi, Md. Atiar Rahman, Sheikh Mahbubur Rahman. Synthesis, characterization, molecular modeling, antioxidant and microbial properties of some Titanium(IV) complexes of schiff bases. Journal of Molecular Structure 2018, 1166 , 79-90. https://doi.org/10.1016/j.molstruc.2018.04.025
    48. Neelima Gupta, Prateek Pandya, Seema Verma. Computational Predictions for Multi-Target Drug Design. 2018, 27-50. https://doi.org/10.1007/7653_2018_26
    49. Jakub Kollar, Vladimir Frecer. How accurate is the description of ligand–protein interactions by a hybrid QM/MM approach?. Journal of Molecular Modeling 2018, 24 (1) https://doi.org/10.1007/s00894-017-3537-z
    50. Michał Nowakowski, Joanna Czapla-Masztafiak, Maciej Kozak, Igor Zhukov, Lilia Zhukova, Jakub Szlachetko, Wojciech M. Kwiatek. Preliminary results of human PrP C protein studied by spectroscopic techniques. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 2017, 411 , 121-128. https://doi.org/10.1016/j.nimb.2017.06.022
    51. Joseph M. Hayes. Computer-Aided Discovery of Glycogen Phosphorylase Inhibitors Exploiting Natural Products. 2017, 29-62. https://doi.org/10.1016/B978-0-12-809450-1.00002-8
    52. Warot Chotpatiwetchkul, Kanokthip Boonyarattanakalin, Duangkamol Gleeson, M. Paul Gleeson. Exploring the catalytic mechanism of dihydropteroate synthase: elucidating the differences between the substrate and inhibitor. Organic & Biomolecular Chemistry 2017, 15 (26) , 5593-5601. https://doi.org/10.1039/C7OB01272A
    53. Jinan Wang, Qiang Shao, Benjamin P. Cossins, Jiye Shi, Kaixian Chen, Weiliang Zhu. Thermodynamics calculation of protein–ligand interactions by QM/MM polarizable charge parameters. Journal of Biomolecular Structure and Dynamics 2016, 34 (1) , 163-176. https://doi.org/10.1080/07391102.2015.1019928
    54. Xinghao Ai, Shengping Shen, Lan Shen, Shun Lu. An interaction map of small-molecule kinase inhibitors with anaplastic lymphoma kinase (ALK) mutants in ALK-positive non-small cell lung cancer. Biochimie 2015, 112 , 111-120. https://doi.org/10.1016/j.biochi.2015.03.003
    55. Duangkamol Gleeson, M. Paul Gleeson. Application of QM/MM and QM methods to investigate histone deacetylase 8. MedChemComm 2015, 6 (3) , 477-485. https://doi.org/10.1039/C4MD00471J
    56. Giulia Palermo, Ursula Rothlisberger, Andrea Cavalli, Marco De Vivo. Computational insights into function and inhibition of fatty acid amide hydrolase. European Journal of Medicinal Chemistry 2015, 91 , 15-26. https://doi.org/10.1016/j.ejmech.2014.09.037
    57. Sunil K. Tripathi, Rajendran Naga Soundarya, Poonam Singh, Sanjeev K. Singh. Comparative Analysis of Various Electrostatic Potentials on Docking Precision Against Cyclin‐Dependent Kinase 2 Protein: A Multiple Docking Approach. Chemical Biology & Drug Design 2015, 85 (2) , 107-118. https://doi.org/10.1111/cbdd.12376
    58. Xinghao Ai, Yingjia Sun, Haidong Wang, Shun Lu. A systematic profile of clinical inhibitors responsive to EGFR somatic amino acid mutations in lung cancer: implication for the molecular mechanism of drug resistance and sensitivity. Amino Acids 2014, 46 (7) , 1635-1648. https://doi.org/10.1007/s00726-014-1716-0
    59. Lianjuan Yang, Xiaohui Mo, Hong Yang, Hejun Dai, Fei Tan. Testing the sensitivities of noncognate inhibitors to varicella zoster virus thymidine kinase: implications for postherpetic neuralgia therapy with existing agents. Journal of Molecular Modeling 2014, 20 (7) https://doi.org/10.1007/s00894-014-2321-6
    60. Jens Antony, Stefan Grimme. Fully ab initio protein‐ligand interaction energies with dispersion corrected density functional theory. Journal of Computational Chemistry 2012, 33 (21) , 1730-1739. https://doi.org/10.1002/jcc.23004
    61. Xinchun Guo, Deyong He, Limin Liu, Renyun Kuang, Lijun Liu. Use of QM/MM scheme to reproduce macromolecule–small molecule noncovalent binding energy. Computational and Theoretical Chemistry 2012, 991 , 134-140. https://doi.org/10.1016/j.comptc.2012.04.010
    62. Pär Söderhjelm, Samuel Genheden, Ulf Ryde. Quantum Mechanics in Structure‐Based Ligand Design. 2012, 121-143. https://doi.org/10.1002/9783527645947.ch7
    63. Thomas C. Schmidt, Alexander Paasche, Christoph Grebner, Kay Ansorg, Johannes Becker, Wook Lee, Bernd Engels. QM/MM Investigations Of Organic Chemistry Oriented Questions. 2012, 25-101. https://doi.org/10.1007/128_2011_309
    64. A. K. Croft, W. Groenewald, M. S. Tierney. Medicinal Chemistry and Ligand Profiling for Evaluation of Promising Marine Bioactive Molecules. 2012, 173-206. https://doi.org/10.1007/978-1-4614-1247-2_7
    65. Duangkamol Gleeson, Ben Tehan, M. Paul Gleeson, Jumras Limtrakul. Evaluating the enthalpic contribution to ligand binding using QM calculations: effect of methodology on geometries and interaction energies. Organic & Biomolecular Chemistry 2012, 10 (35) , 7053. https://doi.org/10.1039/c2ob25657f
    66. M. Paul Gleeson, Supa Hannongbua, Duangkamol Gleeson. QM methods in structure based design: Utility in probing protein–ligand interactions. Journal of Molecular Graphics and Modelling 2010, 29 (4) , 507-517. https://doi.org/10.1016/j.jmgm.2010.09.012
    67. Katherine E. Shaw, Christopher J. Woods, Adrian J. Mulholland. QM and QM / MM Approaches to Evaluating Binding Affinities. 2010, 725-752. https://doi.org/10.1002/0471266949.bmc143
    68. Ricardo A. Mata. Application of high level wavefunction methods in quantum mechanics/molecular mechanics hybrid schemes. Physical Chemistry Chemical Physics 2010, 12 (19) , 5041. https://doi.org/10.1039/b918608e
    69. Richard Lonsdale, Kara E. Ranaghan, Adrian J. Mulholland. Computational enzymology. Chemical Communications 2010, 46 (14) , 2354. https://doi.org/10.1039/b925647d
    70. Yunxiang Lu, Yong Wang, Weiliang Zhu. Nonbonding interactions of organic halogens in biological systems: implications for drug discovery and biomolecular design. Physical Chemistry Chemical Physics 2010, 12 (18) , 4543. https://doi.org/10.1039/b926326h
    71. Kara E. Ranaghan, Adrian J. Mulholland. Investigations of enzyme-catalysed reactions with combined quantum mechanics/molecular mechanics (QM/MM) methods. International Reviews in Physical Chemistry 2010, 29 (1) , 65-133. https://doi.org/10.1080/01442350903495417

    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2009, 49, 3, 670–677
    Click to copy citationCitation copied!
    https://doi.org/10.1021/ci800419j
    Published February 13, 2009
    Copyright © 2009 American Chemical Society

    Article Views

    2386

    Altmetric

    -

    Citations

    Learn about these metrics

    Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

    Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

    The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.