1932

Abstract

Molecular docking has become an essential part of a structural biologist's and medicinal chemist's toolkits. Given a chemical compound and the three-dimensional structure of a molecular target—for example, a protein—docking methods fit the compound into the target, predicting the compound's bound structure and binding energy. Docking can be used to discover novel ligands for a target by screening large virtual compound libraries. Docking can also provide a useful starting point for structure-based ligand optimization or for investigating a ligand's mechanism of action. Advances in computational methods, including both physics-based and machine learning approaches, as well as in complementary experimental techniques, are making docking an even more powerful tool. We review how docking works and how it can drive drug discovery and biological research. We also describe its current limitations and ongoing efforts to overcome them.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-biochem-030222-120000
2024-08-02
2025-04-19
Loading full text...

Full text loading...

/deliver/fulltext/biochem/93/1/annurev-biochem-030222-120000.html?itemId=/content/journals/10.1146/annurev-biochem-030222-120000&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE. 1982.. A geometric approach to macromolecule-ligand interactions. . J. Mol. Biol. 161::26988
    [Crossref] [Google Scholar]
  2. 2.
    Platzer KEB, Momany F, Scheraga H. 1972.. Conformational energy calculations of enzyme-substrate interactions. II. Computation of the binding energy for substrates in the active site of α-chymotrypsin. . Int. J. Peptide Protein Res. 4::20119
    [Crossref] [Google Scholar]
  3. 3.
    Agrawal P, Singh H, Srivastava HK, Singh S, Kishore G, Raghava GPS. 2019.. Benchmarking of different molecular docking methods for protein-peptide docking. . BMC Bioinform. 19::10524
    [Crossref] [Google Scholar]
  4. 4.
    Martin SJ, Chen I-J, Chan AWE, Foloppe N. 2020.. Modelling the binding mode of macrocycles: docking and conformational sampling. . Bioorganic Med. Chem. 28:(1):115143
    [Crossref] [Google Scholar]
  5. 5.
    Porter KA, Desta I, Kozakov D, Vajda S. 2019.. What method to use for protein–protein docking?. Curr. Opin. Struct. Biol. 55::17
    [Crossref] [Google Scholar]
  6. 6.
    Evans R, O'Neill M, Pritzel A, Antropova N, Senior A, et al. 2021.. Protein complex prediction with AlphaFold-Multimer. . bioRxiv 2021.10.04.463034. https://doi.org/10.1101/2021.10.04.463034
  7. 7.
    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, et al. 2004.. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. . J. Med. Chem. 47::173949
    [Crossref] [Google Scholar]
  8. 8.
    Jain AN. 2003.. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. . J. Med. Chem. 46::499511
    [Crossref] [Google Scholar]
  9. 9.
    Trott O, Olson AJ. 2010.. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. . J. Comput. Chem. 31::45561
    [Crossref] [Google Scholar]
  10. 10.
    Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD. 2003.. Improved protein–ligand docking using GOLD. . Proteins 52::60923
    [Crossref] [Google Scholar]
  11. 11.
    Allen WJ, Balius TE, Mukherjee S, Brozell SR, Moustakas DT, et al. 2015.. DOCK 6: impact of new features and current docking performance. . J. Comput. Chem. 36::113256
    [Crossref] [Google Scholar]
  12. 12.
    Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, et al. 2009.. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. . J. Comput. Chem. 30::278591
    [Crossref] [Google Scholar]
  13. 13.
    Coleman RG, Carchia M, Sterling T, Irwin JJ, Shoichet BK. 2013.. Ligand pose and orientational sampling in molecular docking. . PLOS ONE 8::e75992
    [Crossref] [Google Scholar]
  14. 14.
    Grosdidier A, Zoete V, Michielin O. 2011.. SwissDock, a protein-small molecule docking web service based on EADock DSS. . Nucleic Acids Res. 39::W27077
    [Crossref] [Google Scholar]
  15. 15.
    Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP. 1997.. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. . J. Comput.-Aided Mol. Design 11::42545
    [Crossref] [Google Scholar]
  16. 16.
    Koes DR, Baumgartner MP, Camacho CJ. 2013.. Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. . J. Chem. Inform. Model. 53:(8):1893904
    [Crossref] [Google Scholar]
  17. 17.
    Adeshina YO, Deeds EJ, Karanicolas J. 2020.. Machine learning classification can reduce false positives in structure-based virtual screening. . PNAS 117:(31):1847788
    [Crossref] [Google Scholar]
  18. 18.
    Li J, Fu A, Zhang L. 2019.. An overview of scoring functions used for protein–ligand interactions in molecular docking. . Interdiscip. Sci. 11::32028
    [Crossref] [Google Scholar]
  19. 19.
    Ravindranath PA, Forli S, Goodsell DS, Olson AJ, Sanner MF. 2015.. AutoDockFR: advances in protein-ligand docking with explicitly specified binding site flexibility. . PLOS Comput. Biol. 11::e1004586
    [Crossref] [Google Scholar]
  20. 20.
    Pagadala NS, Syed K, Tuszynski J. 2017.. Software for molecular docking: a review. . Biophys. Rev. 9::91102
    [Crossref] [Google Scholar]
  21. 21.
    Clark JJ, Benson ML, Smith RD, Carlson HA. 2019.. Inherent versus induced protein flexibility: comparisons within and between apo and holo structures. . PLOS Comput. Biol. 15::e1006705
    [Crossref] [Google Scholar]
  22. 22.
    Stank A, Kokh DB, Fuller JC, Wade RC. 2016.. Protein binding pocket dynamics. . Acc. Chem. Res. 49::80915
    [Crossref] [Google Scholar]
  23. 23.
    Park H, Zhou G, Baek M, Baker D, DiMaio F. 2021.. Force field optimization guided by small molecule crystal lattice data enables consistent sub-Angstrom protein–ligand docking. . J. Chem. Theory Comput. 17::200010
    [Crossref] [Google Scholar]
  24. 24.
    Sherman W, Day T, Jacobson MP, Friesner RA, Farid R. 2006.. Novel procedure for modeling ligand/receptor induced fit effects. . J. Med. Chem. 49::53453
    [Crossref] [Google Scholar]
  25. 25.
    Kamenik AS, Singh I, Lak P, Balius TE, Liedl KR, Shoichet BK. 2021.. Energy penalties enhance flexible receptor docking in a model cavity. . PNAS 118::e2106195118
    [Crossref] [Google Scholar]
  26. 26.
    Amaro RE, Baudry J, Chodera J, Demir Ö, McCammon JA, et al. 2018.. Ensemble docking in drug discovery. . Biophys. J. 114::227178
    [Crossref] [Google Scholar]
  27. 27.
    Radi M, Dreassi E, Brullo C, Crespan E, Tintori C, et al. 2011.. Design, synthesis, biological activity, and ADME properties of pyrazolo[3,4-d]pyrimidines active in hypoxic human leukemia cells: a lead optimization study. . J. Med. Chem. 54::261026
    [Crossref] [Google Scholar]
  28. 28.
    Shirude PS, Shandil RK, Manjunatha MR, Sadler C, Panda M, et al. 2014.. Lead optimization of 1,4-azaindoles as antimycobacterial agents. . J. Med. Chem. 57::572837
    [Crossref] [Google Scholar]
  29. 29.
    Lv X, Feng L, Ai C-Z, Hou J, Wang P, et al. 2017.. A practical and high-affinity fluorescent probe for uridine diphosphate glucuronosyltransferase 1A1: a good surrogate for bilirubin. . J. Med. Chem. 60::966475
    [Crossref] [Google Scholar]
  30. 30.
    Farina R, Pisani L, Catto M, Nicolotti O, Gadaleta D, et al. 2015.. Structure-based design and optimization of multitarget-directed 2H-chromen-2-one derivatives as potent inhibitors of monoamine oxidase B and cholinesterases. . J. Med. Chem. 58::556178
    [Crossref] [Google Scholar]
  31. 31.
    Zlotos DP, Abdelmalek CM, Botros LS, Banoub MM, Mandour YM, et al. 2021.. C-2-linked dimeric strychnine analogues as bivalent ligands targeting glycine receptors. . J. Nat. Prod. 84::38294
    [Crossref] [Google Scholar]
  32. 32.
    Tu Y, Sun Y, Qiao S, Luo Y, Liu P, et al. 2021.. Design, synthesis, and evaluation of VHL-based EZH2 degraders to enhance therapeutic activity against lymphoma. . J. Med. Chem. 64::1016784
    [Crossref] [Google Scholar]
  33. 33.
    Aboelez MO, Belal A, Xiang G, Ma X. 2022.. Design, synthesis, and molecular docking studies of novel pomalidomide-based PROTACs as potential anti-cancer agents targeting EGFRWT and EGFRT790M. . J. Enzyme Inhib. Med. Chem. 37::1196211
    [Crossref] [Google Scholar]
  34. 34.
    Hellmann J, Drabek M, Yin J, Gunera J, Pröll T, et al. 2020.. Structure-based development of a subtype-selective orexin 1 receptor antagonist. . PNAS 117::1805967
    [Crossref] [Google Scholar]
  35. 35.
    Zhang Z, Min J, Chen M, Jiang X, Xu Y, et al. 2020.. The structure-based optimization of δ-sultone-fused pyrazoles as selective BuChE inhibitors. . Eur. J. Med. Chem. 201::112273
    [Crossref] [Google Scholar]
  36. 36.
    Davies MP, Benitez R, Perez C, Jakupovic S, Welsby P, et al. 2021.. Structure-based design of potent selective nanomolar type-II inhibitors of glycogen synthase kinase-3β. . J. Med. Chem. 64::1497509
    [Crossref] [Google Scholar]
  37. 37.
    Enyedy IJ, Egan WJ. 2008.. Can we use docking and scoring for hit-to-lead optimization?. J. Comput.-Aided Mol. Des. 22::16168
    [Crossref] [Google Scholar]
  38. 38.
    Ramírez D, Caballero J. 2016.. Is it reliable to use common molecular docking methods for comparing the binding affinities of enantiomer pairs for their protein target?. Int. J. Mol. Sci. 17::525
    [Crossref] [Google Scholar]
  39. 39.
    Che T, Majumdar S, Zaidi SA, Ondachi P, McCorvy JD, et al. 2018.. Structure of the nanobody-stabilized active state of the kappa opioid receptor. . Cell 172::5567.e15
    [Crossref] [Google Scholar]
  40. 40.
    Song G, Yang D, Wang Y, de Graaf C, Zhou Q, et al. 2017.. Human GLP-1 receptor transmembrane domain structure in complex with allosteric modulators. . Nature 546::31215
    [Crossref] [Google Scholar]
  41. 41.
    Zhang K, Zhang J, Gao Z-G, Zhang D, Zhu L, et al. 2014.. Structure of the human P2Y12 receptor in complex with an antithrombotic drug. . Nature 509::11518
    [Crossref] [Google Scholar]
  42. 42.
    Taniguchi R, Inoue A, Sayama M, Uwamizu A, Yamashita K, et al. 2017.. Structural insights into ligand recognition by the lysophosphatidic acid receptor LPA6. . Nature 548::35660
    [Crossref] [Google Scholar]
  43. 43.
    El Daibani A, Paggi JM, Kim K, Laloudakis YD, Popov P, et al. 2023.. Molecular mechanism of biased signaling at the kappa opioid receptor. . Nature Commun. 14::1338
    [Crossref] [Google Scholar]
  44. 44.
    Powers AS, Pham V, Burger WA, Thompson G, Laloudakis Y, et al. 2023.. Structural basis of efficacy-driven ligand selectivity at GPCRs. . Nat. Chem. Biol. 19::80514
    [Crossref] [Google Scholar]
  45. 45.
    McCorvy JD, Butler KV, Kelly B, Rechsteiner K, Karpiak J, et al. 2018.. Structure-inspired design of β-arrestin-biased ligands for aminergic GPCRs. . Nat. Chem. Biol. 14::12634
    [Crossref] [Google Scholar]
  46. 46.
    Kelly B, Hollingsworth SA, Blakemore DC, Owen RM, Storer RI, et al. 2021.. Delineating the ligand–receptor interactions that lead to biased signaling at the μ-opioid receptor. . J. Chem. Inform. Model. 61::3696707
    [Crossref] [Google Scholar]
  47. 47.
    Lei T, Hu Z, Ding R, Chen J, Li S, et al. 2019.. Exploring the activation mechanism of a metabotropic glutamate receptor homodimer via molecular dynamics simulation. . ACS Chem. Neurosci. 11::13345
    [Crossref] [Google Scholar]
  48. 48.
    Hollingsworth SA, Dror RO. 2018.. Molecular dynamics simulation for all. . Neuron 99::112943
    [Crossref] [Google Scholar]
  49. 49.
    Bender BJ, Gahbauer S, Luttens A, Lyu J, Webb CM, et al. 2021.. A practical guide to large-scale docking. . Nat. Protoc. 16::4799832
    [Crossref] [Google Scholar]
  50. 50.
    Lyu J, Wang S, Balius TE, Singh I, Levit A, et al. 2019.. Ultra-large library docking for discovering new chemotypes. . Nature 566::22429
    [Crossref] [Google Scholar]
  51. 51.
    Sadybekov AA, Brouillette RL, Marin E, Sadybekov AV, Luginina A, et al. 2020.. Structure-based virtual screening of ultra-large library yields potent antagonists for a lipid GPCR. . Biomolecules 10::1634
    [Crossref] [Google Scholar]
  52. 52.
    Manglik A, Lin H, Aryal DK, McCorvy JD, Dengler D, et al. 2016.. Structure-based discovery of opioid analgesics with reduced side effects. . Nature 537::18590
    [Crossref] [Google Scholar]
  53. 53.
    Singh I, Seth A, Billesbølle CB, Braz J, Rodriguiz RM, et al. 2023.. Structure-based discovery of conformationally selective inhibitors of the serotonin transporter. . Cell 186::216075.e17
    [Crossref] [Google Scholar]
  54. 54.
    Fink EA, Xu J, Hübner H, Braz JM, Seemann P, et al. 2022.. Structure-based discovery of nonopioid analgesics acting through the α2A-adrenergic receptor. . Science 377::eabn7065
    [Crossref] [Google Scholar]
  55. 55.
    Kincaid VA, London N, Wangkanont K, Wesener DA, Marcus SA, et al. 2015.. Virtual screening for UDP-galactopyranose mutase ligands identifies a new class of antimycobacterial agents. . ACS Chem. Biol. 10::220918
    [Crossref] [Google Scholar]
  56. 56.
    Hughes TE, Del Rosario JS, Kapoor A, Yazici AT, Yudin Y, et al. 2019.. Structure-based characterization of novel TRPV5 inhibitors. . eLife 8::e49572
    [Crossref] [Google Scholar]
  57. 57.
    Matricon P, Nguyen AT, Vo DD, Baltos J-A, Jaiteh M, et al. 2023.. Structure-based virtual screening discovers potent and selective adenosine A1 receptor antagonists. . Eur. J. Med. Chem. 257::115419
    [Crossref] [Google Scholar]
  58. 58.
    Liu S-H, Xiao Z, Mishra SK, Mitchell JC, Smith JC, et al. 2022.. Identification of small-molecule inhibitors of fibroblast growth factor 23 signaling via in silico hot spot prediction and molecular docking to α-Klotho. . J. Chem. Inform. Model. 62:(15):362737
    [Crossref] [Google Scholar]
  59. 59.
    Alon A, Lyu J, Braz JM, Tummino TA, Craik V, et al. 2021.. Structures of the σ2 receptor enable docking for bioactive ligand discovery. . Nature 600::75964
    [Crossref] [Google Scholar]
  60. 60.
    Patel N, Huang XP, Grandner JM, Johansson LC, Stauch B, et al. 2020.. Structure-based discovery of potent and selective melatonin receptor agonists. . eLife 9::e53779
    [Crossref] [Google Scholar]
  61. 61.
    Stein RM, Kang HJ, McCorvy JD, Glatfelter GC, Jones AJ, et al. 2020.. Virtual discovery of melatonin receptor ligands to modulate circadian rhythms. . Nature 579::60914
    [Crossref] [Google Scholar]
  62. 62.
    Irwin JJ, Tang KG, Young J, Dandarchuluun C, Wong BR, et al. 2020.. ZINC20—a free ultralarge-scale chemical database for ligand discovery. . J. Chem. Inform. Model. 60::606573
    [Crossref] [Google Scholar]
  63. 63.
    Grygorenko OO, Radchenko DS, Dziuba I, Chuprina A, Gubina KE, Moroz YS. 2020.. Generating multibillion chemical space of readily accessible screening compounds. . iScience 23:(11):101681
    [Crossref] [Google Scholar]
  64. 64.
    Lyu J, Irwin JJ, Shoichet BK. 2023.. Modeling the expansion of virtual screening libraries. . Nat. Chem. Biol. 19:(6):71218
    [Crossref] [Google Scholar]
  65. 65.
    Gimeno A, Ojeda-Montes MJ, Tomás-Hernández S, Cereto-Massagué A, Beltrán-Debón R, et al. 2019.. The light and dark sides of virtual screening: What is there to know?. Int. J. Mol. Sci. 20::1375
    [Crossref] [Google Scholar]
  66. 66.
    Lapillo M, Tuccinardi T, Martinelli A, Macchia M, Giordano A, Poli G. 2019.. Extensive reliability evaluation of docking-based target-fishing strategies. . Int. J. Mol. Sci. 20::1023
    [Crossref] [Google Scholar]
  67. 67.
    Kharkar PS, Warrier S, Gaud RS. 2014.. Reverse docking: a powerful tool for drug repositioning and drug rescue. . Future Med. Chem. 6::33342
    [Crossref] [Google Scholar]
  68. 68.
    Ahmed M, Khan K-u-R, Ahmad S, Aati HY, Ovatlarnporn C, et al. 2022.. Comprehensive phytochemical profiling, biological activities, and molecular docking studies of Pleurospermum candollei: an insight into potential for natural products development. . Molecules 27::4113
    [Crossref] [Google Scholar]
  69. 69.
    Zhao S, Kumar R, Sakai A, Vetting MW, Wood BM, et al. 2013.. Discovery of new enzymes and metabolic pathways by using structure and genome context. . Nature 502::698702
    [Crossref] [Google Scholar]
  70. 70.
    Grinter SZ, Liang Y, Huang S-Y, Hyder SM, Zou X. 2011.. An inverse docking approach for identifying new potential anti-cancer targets. . J. Mol. Graph. Model. 29::79599
    [Crossref] [Google Scholar]
  71. 71.
    Raveh B, Sun L, White KL, Sanyal T, Tempkin J, et al. 2021.. Bayesian metamodeling of complex biological systems across varying representations. . PNAS 118::e2104559118
    [Crossref] [Google Scholar]
  72. 72.
    Saller J, List C, Hübner H, Gmeiner P, Clark T, Pischetsrieder M. 2023.. Identification and quantification of kukoamine A and kukoamine B as novel μ-opioid receptor agonists in potato and other solanaceous plants. . Food Chem. 427::136637
    [Crossref] [Google Scholar]
  73. 73.
    Robertson MJ, van Zundert GC, Borrelli K, Skiniotis G. 2020.. GemSpot: a pipeline for robust modeling of ligands into cryo-EM maps. . Structure 28::70716.e3
    [Crossref] [Google Scholar]
  74. 74.
    Malhotra S, Karanicolas J. 2017.. When does chemical elaboration induce a ligand to change its binding mode?. J. Med. Chem. 60::12845
    [Crossref] [Google Scholar]
  75. 75.
    McNutt AT, Francoeur P, Aggarwal R, Masuda T, Meli R, et al. 2021.. GNINA 1.0: molecular docking with deep learning. . J. Cheminformatics 13::43
    [Crossref] [Google Scholar]
  76. 76.
    Stafford KA, Anderson BM, Sorenson J, van den Bedem H. 2022.. AtomNet PoseRanker: enriching ligand pose quality for dynamic proteins in virtual high-throughput screens. . J. Chem. Inform. Model. 62::117889
    [Crossref] [Google Scholar]
  77. 77.
    Karelina M, Noh JJ, Dror RO. 2023.. How accurately can one predict drug binding modes using AlphaFold models?. eLife 12::RP89386
    [Crossref] [Google Scholar]
  78. 78.
    Holcomb M, Chang YT, Goodsell DS, Forli S. 2023.. Evaluation of AlphaFold2 structures as docking targets. . Protein Sci. 32::e4530
    [Crossref] [Google Scholar]
  79. 79.
    Wang Z, Sun H, Yao X, Li D, Xu L, et al. 2016.. Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power. . Phys. Chem. Chem. Phys. 18::1296475
    [Crossref] [Google Scholar]
  80. 80.
    Su M, Yang Q, Du Y, Feng G, Liu Z, et al. 2018.. Comparative assessment of scoring functions: the CASF-2016 update. . J. Chem. Inform. Model. 59::895913
    [Crossref] [Google Scholar]
  81. 81.
    Fierro F, Peri L, Hübner H, Tabor-Schkade A, Waterloo L, et al. 2023.. Inhibiting a promiscuous GPCR: iterative discovery of bitter taste receptor ligands. . Cell. Mol. Life Sci. 80::114
    [Crossref] [Google Scholar]
  82. 82.
    Gahbauer S, Correy GJ, Schuller M, Ferla MP, Doruk YU, et al. 2023.. Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 macrodomain of SARS-CoV-2. . PNAS 120::e2212931120
    [Crossref] [Google Scholar]
  83. 83.
    Huang X-P, Karpiak J, Kroeze WK, Zhu H, Chen X, et al. 2015.. Allosteric ligands for the pharmacologically dark receptors GPR68 and GPR65. . Nature 527::47783
    [Crossref] [Google Scholar]
  84. 84.
    Carlsson J, Coleman RG, Setola V, Irwin JJ, Fan H, et al. 2011.. Ligand discovery from a dopamine D3 receptor homology model and crystal structure. . Nat. Chem. Biol. 7::76978
    [Crossref] [Google Scholar]
  85. 85.
    Lansu K, Karpiak J, Liu J, Huang X-P, McCorvy JD, et al. 2017.. In silico design of novel probes for the atypical opioid receptor MRGPRX2. . Nat. Chem. Biol. 13::52936
    [Crossref] [Google Scholar]
  86. 86.
    Scardino V, Di Filippo JI, Cavasotto CN. 2023.. How good are AlphaFold models for docking-based virtual screening?. iScience 26:(1):105920
    [Crossref] [Google Scholar]
  87. 87.
    Zhang Y, Vass M, Shi D, Abualrous E, Chambers JM, et al. 2023.. Benchmarking refined and unrefined AlphaFold2 structures for hit discovery. . J. Chem. Inform. Model. 63::165667
    [Crossref] [Google Scholar]
  88. 88.
    Gorgulla C, Boeszoermenyi A, Wang Z-F, Fischer PD, Coote PW, et al. 2020.. An open-source drug discovery platform enables ultra-large virtual screens. . Nature 580::66368
    [Crossref] [Google Scholar]
  89. 89.
    Fischer A, Smiesko M, Sellner M, Lill MA. 2021.. Decision making in structure-based drug discovery: visual inspection of docking results. . J. Med. Chem. 64::2489500
    [Crossref] [Google Scholar]
  90. 90.
    Weiss DR, Karpiak J, Huang X-P, Sassano MF, Lyu J, et al. 2018.. Selectivity challenges in docking screens for GPCR targets and antitargets. . J. Med. Chem. 61:(15):683045
    [Crossref] [Google Scholar]
  91. 91.
    Kozakov D, Hall DR, Jehle S, Luo L, Ochiana SO, et al. 2015.. Ligand deconstruction: why some fragment binding positions are conserved and others are not. . PNAS 112::E258594
    [Crossref] [Google Scholar]
  92. 92.
    Pottel J, Levit A, Korczynska M, Fischer M, Shoichet BK. 2018.. The recognition of unrelated ligands by identical proteins. . ACS Chem. Biol. 13::252233
    [Crossref] [Google Scholar]
  93. 93.
    Fu DY, Meiler J. 2018.. RosettaLigandEnsemble: a small-molecule ensemble-driven docking approach. . ACS Omega 3::365564
    [Crossref] [Google Scholar]
  94. 94.
    Paggi JM, Belk JA, Hollingsworth SA, Villanueva N, Powers AS, et al. 2021.. Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes. . PNAS 118::e2112621118
    [Crossref] [Google Scholar]
  95. 95.
    Pisa R, Cupido T, Steinman JB, Jones NH, Kapoor TM. 2019.. Analyzing resistance to design selective chemical inhibitors for AAA proteins. . Cell Chem. Biol. 26::126373.e5
    [Crossref] [Google Scholar]
  96. 96.
    Thomas-Tran R, Du Bois J. 2016.. Mutant cycle analysis with modified saxitoxins reveals specific interactions critical to attaining high-affinity inhibition of hNaV1.7. . PNAS 113::585661
    [Crossref] [Google Scholar]
  97. 97.
    Rahman S, Luetje CW. 2017.. Mutant cycle analysis identifies a ligand interaction site in an odorant receptor of the malaria vector Anopheles gambiae. . J. Biol. Chem. 292::1891623
    [Crossref] [Google Scholar]
  98. 98.
    Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, et al. 2017.. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. . Nat. Methods 14:(1):7173
    [Crossref] [Google Scholar]
  99. 99.
    D'Amore L, Hahn DF, Dotson DL, Horton JT, Anwar J, et al. 2022.. Collaborative assessment of molecular geometries and energies from the Open Force Field. . J. Chem. Inform. Model. 62::6094104
    [Crossref] [Google Scholar]
  100. 100.
    Dror RO, Pan AC, Arlow DH, Borhani DW, Maragakis P, et al. 2011.. Pathway and mechanism of drug binding to G-protein-coupled receptors. . PNAS 108:(32):1311823
    [Crossref] [Google Scholar]
  101. 101.
    Dror RO, Green HF, Valant C, Borhani DW, Valcourt JR, et al. 2013.. Structural basis for modulation of a G-protein-coupled receptor by allosteric drugs. . Nature 503:(7475):29599
    [Crossref] [Google Scholar]
  102. 102.
    Shan Y, Kim ET, Eastwood MP, Dror RO, Seeliger MA, Shaw DE. 2011.. How does a drug molecule find its target binding site?. J. Am. Chem. Soc. 133::918183
    [Crossref] [Google Scholar]
  103. 103.
    Pan AC, Xu H, Palpant T, Shaw DE. 2017.. Quantitative characterization of the binding and unbinding of millimolar drug fragments with molecular dynamics simulations. . J. Chem. Theory Comput. 13::337277
    [Crossref] [Google Scholar]
  104. 104.
    Cournia Z, Allen B, Sherman W. 2017.. Relative binding free energy calculations in drug discovery: recent advances and practical considerations. . J. Chem. Inform. Model. 57::291137
    [Crossref] [Google Scholar]
  105. 105.
    Li Z, Huang Y, Wu Y, Chen J, Wu D, et al. 2019.. Absolute binding free energy calculation and design of a subnanomolar inhibitor of phosphodiesterase-10. . J. Med. Chem. 62::2099111
    [Crossref] [Google Scholar]
  106. 106.
    Cournia Z, Allen BK, Beuming T, Pearlman DA, Radak BK, Sherman W. 2020.. Rigorous free energy simulations in virtual screening. . J. Chem. Inform. Model. 60::415369
    [Crossref] [Google Scholar]
  107. 107.
    Heinzelmann G, Gilson MK. 2021.. Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation. . Sci. Rep. 11::1116
    [Crossref] [Google Scholar]
  108. 108.
    Miller EB, Murphy RB, Sindhikara D, Borrelli KW, Grisewood MJ, et al. 2021.. Reliable and accurate solution to the induced fit docking problem for protein–ligand binding. . J. Chem. Theory Comput. 17::263039
    [Crossref] [Google Scholar]
  109. 109.
    Hsu DJ, Davidson RB, Sedova A, Glaser J. 2022.. tinyIFD: a high-throughput binding pose refinement workflow through induced-fit ligand docking. . J. Chem. Inform. Model. 63:(11):343847
    [Crossref] [Google Scholar]
  110. 110.
    Abel R, Young T, Farid R, Berne BJ, Friesner RA. 2008.. Role of the active-site solvent in the thermodynamics of factor Xa ligand binding. . J. Am. Chem. Soc. 130::281731
    [Crossref] [Google Scholar]
  111. 111.
    Balius TE, Fischer M, Stein RM, Adler TB, Nguyen CN, et al. 2017.. Testing inhomogeneous solvation theory in structure-based ligand discovery. . PNAS 114::E683946
    [Crossref] [Google Scholar]
  112. 112.
    Uehara S, Tanaka S. 2016.. AutoDock-GIST: incorporating thermodynamics of active-site water into scoring function for accurate protein-ligand docking. . Molecules 21::1604
    [Crossref] [Google Scholar]
  113. 113.
    Arcon JP, Defelipe LA, Modenutti CP, López ED, Alvarez-Garcia D, et al. 2017.. Molecular dynamics in mixed solvents reveals protein–ligand interactions, improves docking, and allows accurate binding free energy predictions. . J. Chem. Inform. Model. 57::84663
    [Crossref] [Google Scholar]
  114. 114.
    Smith RD, Carlson HA. 2021.. Identification of cryptic binding sites using MixMD with standard and accelerated molecular dynamics. . J. Chem. Inform. Model. 61::128799
    [Crossref] [Google Scholar]
  115. 115.
    Durrant JD, McCammon JA. 2010.. NNScore: a neural-network-based scoring function for the characterization of protein−ligand complexes. . J. Chem. Inform. Model. 50::186571
    [Crossref] [Google Scholar]
  116. 116.
    Feinberg EN, Sur D, Wu Z, Husic BE, Mai H, et al. 2018.. PotentialNet for molecular property prediction. . ACS Central Sci. 4::152030
    [Crossref] [Google Scholar]
  117. 117.
    Francoeur PG, Masuda T, Sunseri J, Jia A, Iovanisci RB, et al. 2020.. Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design. . J. Chem. Inform. Model. 60::420015
    [Crossref] [Google Scholar]
  118. 118.
    Jiménez J, Škalič M, Martínez-Rosell G, De Fabritiis G. 2018.. KDEEP: protein–ligand absolute binding affinity prediction via 3D-convolutional neural networks. . J. Chem. Inform. Model. 58::28796
    [Crossref] [Google Scholar]
  119. 119.
    Wallach I, Dzamba M, Heifets A. 2015.. AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery. . arXiv.1510.02855 [cs.LG]
  120. 120.
    Ballester PJ, Mitchell JBO. 2010.. A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. . Bioinformatics 26::116975
    [Crossref] [Google Scholar]
  121. 121.
    Mark AE, Van Gunsteren WF. 1994.. Decomposition of the free energy of a system in terms of specific interactions: implications for theoretical and experimental studies. . J. Mol. Biol. 240::16776
    [Crossref] [Google Scholar]
  122. 122.
    Suriana P, Paggi JM, Dror RO. 2023.. FlexVDW: a machine learning approach to account for protein flexibility in ligand docking. . arXiv.2303.11494 [q-bio.BM]
  123. 123.
    Masters MR, Mahmoud AH, Wei Y, Lill MA. 2023.. Deep learning model for efficient protein–ligand docking with implicit side-chain flexibility. . J. Chem. Inform. Model. 63::1695707
    [Crossref] [Google Scholar]
  124. 124.
    Liu Z, Su M, Han L, Liu J, Yang Q, et al. 2017.. Forging the basis for developing protein–ligand interaction scoring functions. . Acc. Chem. Res. 50::3029
    [Crossref] [Google Scholar]
  125. 125.
    Adeshina YO, Deeds EJ, Karanicolas J. 2020.. Machine learning classification can reduce false positives in structure-based virtual screening. . PNAS 117::1847788
    [Crossref] [Google Scholar]
  126. 126.
    Yang Y, Yao K, Repasky MP, Leswing K, Abel R, et al. 2021.. Efficient exploration of chemical space with docking and deep learning. . J. Chem. Theory Comput. 17::710619
    [Crossref] [Google Scholar]
  127. 127.
    Gentile F, Yaacoub JC, Gleave J, Fernandez M, Ton A-T, et al. 2022.. Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking. . Nat. Protoc. 17::67297
    [Crossref] [Google Scholar]
  128. 128.
    Graff DE, Shakhnovich EI, Coley CW. 2021.. Accelerating high-throughput virtual screening through molecular pool-based active learning. . Chem. Sci. 12::786681
    [Crossref] [Google Scholar]
  129. 129.
    Thompson J, Walters WP, Feng JA, Pabon NA, Xu H, et al. 2022.. Optimizing active learning for free energy calculations. . Artif. Intel. Life Sci. 2::100050
    [Google Scholar]
  130. 130.
    Khalak Y, Tresadern G, Hahn DF, de Groot BL, Gapsys V. 2022.. Chemical space exploration with active learning and alchemical free energies. . J. Chem. Theory Comput. 18::625970
    [Crossref] [Google Scholar]
  131. 131.
    Thomas M, Bender A, de Graaf C. 2023.. Integrating structure-based approaches in generative molecular design. . Curr. Opin. Struct. Biol. 79::102559
    [Crossref] [Google Scholar]
  132. 132.
    Beroza P, Crawford JJ, Ganichkin O, Gendelev L, Harris SF, et al. 2022.. Chemical space docking enables large-scale structure-based virtual screening to discover ROCK1 kinase inhibitors. . Nat. Commun. 13::6447
    [Crossref] [Google Scholar]
  133. 133.
    Sadybekov AA, Sadybekov AV, Liu Y, Iliopoulos-Tsoutsouvas C, Huang X-P, et al. 2021.. Synthon-based ligand discovery in virtual libraries of over 11 billion compounds. . Nature 601::45259
    [Crossref] [Google Scholar]
  134. 134.
    Spiegel JO, Durrant JD. 2020.. AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization. . J. Cheminformatics 12::25
    [Crossref] [Google Scholar]
  135. 135.
    Powers AS, Yu HH, Suriana P, Koodli RV, Lu T, et al. 2023.. Geometric deep learning for structure-based ligand design. . ACS Cent. Sci. 9::225767
    [Crossref] [Google Scholar]
  136. 136.
    Stärk H, Ganea O, Pattanaik L, Barzilay R, Jaakkola T. 2022.. Equibind: geometric deep learning for drug binding structure prediction. . Proc. Mach. Learn. Res. 162::2050321
    [Google Scholar]
  137. 137.
    Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. 2022.. DiffDock: diffusion steps, twists, and turns for molecular docking. . arXiv.2210.01776 [q-bio.BM]
  138. 138.
    Krishna R, Wang J, Ahern W, Sturmfels P, Venkatesh P, et al. 2023.. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. . bioRxiv 2023.10.09.561603. https://doi.org/10.1101/2023.10.09.561603
  139. 139.
    Yu Y, Lu S, Gao Z, Zheng H, Ke G. 2023.. Do deep learning models really outperform traditional approaches in molecular docking?. arXiv.2302.07134 [q-bio.BM]
  140. 140.
    Buttenschoen M, Morris GM, Deane CM. 2023.. PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. . arXiv.2308.05777 [q-bio.QM]
  141. 141.
    Brocidiacono M, Francoeur P, Aggarwal R, Popov K, Koes D, Tropsha A. 2022.. BigBind: learning from nonstructural data for structure-based virtual screening. . ChemRxiv chemrxiv-2022-3qc9t-v3. http://doi.org/10.26434/chemrxiv-2022-3qc9t-v2
  142. 142.
    Torng W, Altman RB. 2019.. Graph convolutional neural networks for predicting drug-target interactions. . J. Chem. Inform. Model. 59::413149
    [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-biochem-030222-120000
Loading
/content/journals/10.1146/annurev-biochem-030222-120000
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error