An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking

  • Jin Li
  • Ailing Fu
  • Le ZhangEmail author


Currently, molecular docking is becoming a key tool in drug discovery and molecular modeling applications. The reliability of molecular docking depends on the accuracy of the adopted scoring function, which can guide and determine the ligand poses when thousands of possible poses of ligand are generated. The scoring function can be used to determine the binding mode and site of a ligand, predict binding affinity and identify the potential drug leads for a given protein target. Despite intensive research over the years, accurate and rapid prediction of protein–ligand interactions is still a challenge in molecular docking. For this reason, this study reviews four basic types of scoring functions, physics-based, empirical, knowledge-based, and machine learning-based scoring functions, based on an up-to-date classification scheme. We not only discuss the foundations of the four types scoring functions, suitable application areas and shortcomings, but also discuss challenges and potential future study directions.


Molecular docking Scoring function Ligand pose Binding affinity Protein–ligand interaction 



Scoring function


Quantum mechanics


Molecular mechanics


Support vector machine


Random forest


Artificial neural network


Deep learning


Deep neural networks



This study is supported by the National Natural Science Foundation of China (No. 61372138), and National Science and Technology Major Project of China (No. 2018ZX10201002).

Author contributions

Conception and design: LZ; Writing and revision of the manuscript: JL; ALF.


This study is supported by the National Natural Science Foundation of China (No. 61372138), and National Science and Technology Major Project of China (No. 2018ZX10201002).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflicts of interest.


  1. 1.
    Irwin JJ, Lorber DM, Mcgovern SL, Wei B, Shoichet BK (2002) Molecular docking and drug design. Comput Nanosci Nanotechnol 2:50–51Google Scholar
  2. 2.
    Fenu LALR, Good AC, Bodkin M, Essex JW (2007) Structure-based drug discovery. Springer, Dordrecht, p 24Google Scholar
  3. 3.
    Huang SY, Grinter SZ, Zou X (2010) Scoring functions and their evaluation methods for protein–ligand docking: recent advances and future directions. Phys Chem Chem Phys PCCP 12(40):12899–12908. PubMedCrossRefGoogle Scholar
  4. 4.
    Brooijmans N, Kuntz ID (2003) Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct 32:335–373. PubMedCrossRefGoogle Scholar
  5. 5.
    Wang JC, Lin JH (2013) Scoring functions for prediction of protein–ligand interactions. Curr Pharm Design 19(12):2174–2182CrossRefGoogle Scholar
  6. 6.
    Hermann JC, Marti-Arbona R, Fedorov AA, Fedorov E, Almo SC, Shoichet BK, Raushel FM (2007) Structure-based activity prediction for an enzyme of unknown function. Nature 448(7155):775–779PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Joseph-Mccarthy D, Baber JC, Feyfant E, Thompson DC, Humblet C (2007) Lead optimization via high-throughput molecular docking. Curr Opin Drug Discov Devel 10(3):264–274PubMedGoogle Scholar
  8. 8.
    Jorgensen WL (2009) Efficient drug lead discovery and optimization. Acc Chem Res 42(6):724–733PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Seifert MH, Kraus J, Kramer B (2007) Virtual high-throughput screening of molecular databases. Curr Opin Drug Discov Devel 10(3):298–307PubMedGoogle Scholar
  10. 10.
    Schneider G (2010) Virtual screening: an endless staircase? Nat Rev Drug Discov 9(4):273–276. PubMedCrossRefGoogle Scholar
  11. 11.
    Ain QU, Aleksandrova A, Roessler FD, Ballester PJ (2015) Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdiscip Rev Comput Mol Sci 5(6):405–424. PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    Khamis MA, Gomaa W, Ahmed WF (2015) Machine learning in computational docking. Artif Intell Med 63(3):135–152. PubMedCrossRefGoogle Scholar
  13. 13.
    Liu J, Wang R (2015) Classification of current scoring functions. J Chem Inf Model 55(3):475PubMedCrossRefGoogle Scholar
  14. 14.
    Meng EC, Shoichet BK, Kuntz ID (1992) Automated docking with grid-based energy evaluation, vol 13. Wiley, New YorkGoogle Scholar
  15. 15.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935CrossRefGoogle Scholar
  16. 16.
    Pullman B (1981) Intermolecular forces. D. Reidel Publishing Company, DordrechtCrossRefGoogle Scholar
  17. 17.
    Raha K, Peters MB, Wang B, Yu N, Wollacott AM, Westerhoff LM, Merz KM Jr (2007) The role of quantum mechanics in structure-based drug design. Drug Discov Today 12(17–18):725–731. PubMedCrossRefGoogle Scholar
  18. 18.
    Senn HM, Thiel W (2009) QM/MM methods for biomolecular systems. Angew Chem (Int Edn Engl) 48(7):1198–1229. CrossRefGoogle Scholar
  19. 19.
    Kramer B, Rarey M, Lengauer T (1999) Evaluation of the FLEXX incremental construction algorithm for protein–ligand docking. Proteins Struct Funct Bioinf 37(2):228–241CrossRefGoogle Scholar
  20. 20.
    Yang Y, Lightstone FC, Wong SE (2013) Approaches to efficiently estimate solvation and explicit water energetics in ligand binding: the use of WaterMap. Expert Opin Drug Discov 8(3):277–287. PubMedCrossRefGoogle Scholar
  21. 21.
    Michel J, Tirado-Rives J, Jorgensen WL (2009) Prediction of the water content in protein binding sites. J Phys Chem B 113(40):13337–13346. PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Ross GA, Morris GM, Biggin PC (2012) Rapid and accurate prediction and scoring of water molecules in protein binding sites. PLoS One 7(3):e32036. PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Uehara S, Tanaka S (2016) AutoDock-GIST: incorporating thermodynamics of active-site water into scoring function for accurate protein–ligand docking. Molecules. PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Kumar A, Zhang KY (2013) Investigation on the effect of key water molecules on docking performance in CSARdock exercise. J Chem Inf Model 53(8):1880–1892PubMedCrossRefGoogle Scholar
  25. 25.
    Sun H, Li Y, Li D, Hou T (2013) Insight into crizotinib resistance mechanisms caused by three mutations in ALK tyrosine kinase using free energy calculation approaches. J Chem Inf Model 53(9):2376–2389. PubMedCrossRefGoogle Scholar
  26. 26.
    Sun HY, Hou TJ, Zhang HY (2014) Finding chemical drugs for genetic diseases. Drug Discov Today 19(12):1836–1840. PubMedCrossRefGoogle Scholar
  27. 27.
    Chen F, Liu H, Sun H, Pan P, Li Y, Li D, Hou T (2016) Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein–protein binding free energies and re-rank binding poses generated by protein–protein docking. Phys Chem Chem Phys PCCP 18(32):22129–22139. PubMedCrossRefGoogle Scholar
  28. 28.
    Kulik HJ (2018) Large-scale QM/MM free energy simulations of enzyme catalysis reveal the influence of charge transfer. Phys Chem Chem Phys PCCP 20(31):20650–20660. PubMedCrossRefGoogle Scholar
  29. 29.
    Orozco-Gonzalez Y, Manathunga M, Marin MDC, Agathangelou D, Jung KH, Melaccio F, Ferre N, Haacke S, Coutinho K, Canuto S, Olivucci M (2017) An average solvent electrostatic configuration protocol for QM/MM free energy optimization: implementation and application to rhodopsin systems. J Chem Theory Comput 13(12):6391–6404. PubMedCrossRefGoogle Scholar
  30. 30.
    Chaskar P, Zoete V, Röhrig UF (2017) On-the-Fly QM/MM docking with attracting cavities. J Chem Inf Model 57(1):73–84. PubMedCrossRefGoogle Scholar
  31. 31.
    Natesan S, Subramaniam R, Bergeron C, Balaz S (2012) Binding affinity prediction for ligands and receptors forming tautomers and ionization species: inhibition of mitogen-activated protein kinase-activated protein kinase 2 (MK2). J Med Chem 55(5):2035–2047. PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Chaskar P, Zoete V, Rohrig UF (2014) Toward on-the-fly quantum mechanical/molecular mechanical (QM/MM) docking: development and benchmark of a scoring function. J Chem Inf Model 54(11):3137–3152. PubMedCrossRefGoogle Scholar
  33. 33.
    Steinmann C, Olsson MA, Ryde U (2018) Relative ligand-binding free energies calculated from multiple short QM/MM MD simulations. Acs Nano 14(6):3228–3237Google Scholar
  34. 34.
    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(5):425–445CrossRefGoogle Scholar
  35. 35.
    Murray CW, Auton TR, Eldridge MD (1998) Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model. J Comput Aided Mol Design 12(5):503–519CrossRefGoogle Scholar
  36. 36.
    Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J Med Chem 49(21):6177–6196. PubMedCrossRefGoogle Scholar
  37. 37.
    Zheng Z, Merz KM (2011) Ligand identification scoring algorithm (LISA). J Chem Inf Model 51(6):1296–1306. PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Kadukova M, Grudinin S (2017) Convex-PL: a novel knowledge-based potential for protein–ligand interactions deduced from structural databases using convex optimization. J Comput Aided Mol Design 31(10):943–958. CrossRefGoogle Scholar
  39. 39.
    Fornabaio M, Spyrakis F, Mozzarelli A, Cozzini P, Abraham DJ, Kellogg GE (2004) Simple, intuitive calculations of free energy of binding for protein–ligand complexes. 3. The free energy contribution of structural water molecules in HIV-1 protease complexes. J Med Chem 47(18):4507–4516. PubMedCrossRefGoogle Scholar
  40. 40.
    Kerzmann A, Neumann D, Kohlbacher O (2006) SLICK—scoring and energy functions for protein–carbohydrate interactions. J Chem Inf Model 46(4):1635–1642. PubMedCrossRefGoogle Scholar
  41. 41.
    Catana CS, Novel PFW (2007) Customizable scoring functions, parameterized using N-PLS, for structure-based drug discovery. J Chem Inf Model 47(1):85–91PubMedCrossRefGoogle Scholar
  42. 42.
    Sotriffer CA, Sanschagrin P, Matter H, Klebe G (2008) SFCscore: scoring functions for affinity prediction of protein–ligand complexes. Proteins 73(2):395–419. PubMedCrossRefGoogle Scholar
  43. 43.
    Bohm HJ (1994) The development of a simple empirical scoring function to estimate the binding constant for a protein–ligand complex of known three-dimensional structure. J Comput Aided Mol Design 8(3):243–256CrossRefGoogle Scholar
  44. 44.
    Jain AN (2003) Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 46(4):499–511. PubMedCrossRefGoogle Scholar
  45. 45.
    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(2):455–461. PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Li Y, Liu Z, Li J, Han L, Liu J, Zhao Z, Wang R (2014) Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set. J Chem Inf Model 54(6):1700–1716. PubMedCrossRefGoogle Scholar
  47. 47.
    Thornton BF, Wik M, Crill PM (2017) Double-counting challenges the accuracy of high-latitude methane inventories. Geophys Res Lett 43:(24)Google Scholar
  48. 48.
    Muegge I, Martin YC (1999) A general and fast scoring function for protein–ligand interactions: a simplified potential approach. J Med Chem 42(5):791–804. PubMedCrossRefGoogle Scholar
  49. 49.
    Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein–ligand interactions. J Mol Biol 295(2):337–356. PubMedCrossRefGoogle Scholar
  50. 50.
    Velec HF, Gohlke H, Klebe G (2005) DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J Med Chem 48(20):6296–6303. PubMedCrossRefGoogle Scholar
  51. 51.
    Neudert G, Klebe G (2011) DSX: a knowledge-based scoring function for the assessment of protein–ligand complexes. J Chem Inf Model 51(10):2731–2745. PubMedCrossRefGoogle Scholar
  52. 52.
    Mooij WT, Verdonk ML (2005) General and targeted statistical potentials for protein–ligand interactions. Proteins 61(2):272–287. PubMedCrossRefGoogle Scholar
  53. 53.
    Yang CY, Wang R, Wang S (2006) M-score: a knowledge-based potential scoring function accounting for protein atom mobility. J Med Chem 49(20):5903–5911. PubMedCrossRefGoogle Scholar
  54. 54.
    Huang SY, Zou X (2006) An iterative knowledge-based scoring function to predict protein–ligand interactions: I. Derivation of interaction potentials. J Comput Chem 27(15):1866–1875. PubMedCrossRefGoogle Scholar
  55. 55.
    Huang SY, Zou X (2006) An iterative knowledge-based scoring function to predict protein–ligand interactions: II. Validation of the scoring function. J Comput Chem 27(15):1876–1882. PubMedCrossRefGoogle Scholar
  56. 56.
    Huang SY, Zou X (2014) A knowledge-based scoring function for protein–RNA interactions derived from a statistical mechanics-based iterative method. Nucleic Acids Res 42(7):e55. PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Forli S, Olson AJ (2012) A force field with discrete displaceable waters and desolvation entropy for hydrated ligand docking. J Med Chem 55(2):623–638. PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Huang SY, Zou X (2010) Inclusion of solvation and entropy in the knowledge-based scoring function for protein–ligand interactions. J Chem Inf Model 50(2):262–273. PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Lu M, Dousis AD, Ma J (2008) OPUS-PSP: an orientation-dependent statistical all-atom potential derived from side-chain packing. J Mol Biol 376(1):288–301. PubMedCrossRefGoogle Scholar
  60. 60.
    Xu G, Ma T, Zang T, Sun W, Wang Q, Ma J (2017) OPUS-DOSP: a distance- and orientation-dependent all-atom potential derived from side-chain packing. J Mol Biol 429(20):3113–3120. PubMedPubMedCentralCrossRefGoogle Scholar
  61. 61.
    Li Y, Han L, Liu Z, Wang R (2014) Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results. J Chem Inf Model 54(6):1717–1736. PubMedCrossRefGoogle Scholar
  62. 62.
    Park J, Saitou K (2014) ROTAS: a rotamer-dependent, atomic statistical potential for assessment and prediction of protein structures. BMC Bioinform 15:307. CrossRefGoogle Scholar
  63. 63.
    Zheng Z, Merz KM Jr (2013) Development of the knowledge-based and empirical combined scoring algorithm (KECSA) to score protein–ligand interactions. J Chem Inf Model 53(5):1073–1083. PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Ma DL, Chan DS, Leung CH (2013) Drug repositioning by structure-based virtual screening. Chem Society Rev 42(5):2130–2141. CrossRefGoogle Scholar
  65. 65.
    Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH (2012) Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J 14(1):133–141. PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Zhang L, Ai HX, Li SM, Qi MY, Zhao J, Zhao Q, Liu HS (2017) Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function. Oncotarget 8(47):83142–83154. PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Zhang L, Qiao M, Gao H, Hu B, Tan H, Zhou X, Li CM (2016) Investigation of mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model and experimental optimization/validation. Nanoscale 8(31):14877–14887. PubMedCrossRefGoogle Scholar
  68. 68.
    Zhang L, Zhang S (2017) Using game theory to investigate the epigenetic control mechanisms of embryo development: Comment on: “Epigenetic game theory: How to compute the epigenetic control of maternal-to-zygotic transition” by Qian Wang et al. Phys Life Rev 20:140–142. PubMedCrossRefGoogle Scholar
  69. 69.
    Zhang L, Zheng CQ, Li T, Xing L, Zeng H, Li TT, Yang H, Cao J, Chen BD, Zhou ZY (2017) Building up a robust risk mathematical platform to predict colorectal cancer. Complexity 2017:14. CrossRefGoogle Scholar
  70. 70.
    Kinnings SL, Liu N, Tonge PJ, Jackson RM, Xie L, Bourne PE (2011) A machine learning-based method to improve docking scoring functions and its application to drug repurposing. J Chem Inf Model 51(2):408–419. PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Brylinski M (2013) Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. J Chem Inf Model 53(11):3097–3112. PubMedCrossRefGoogle Scholar
  72. 72.
    Li GB, Yang LL, Wang WJ, Li LL, Yang SY (2013) ID-Score: a new empirical scoring function based on a comprehensive set of descriptors related to protein–ligand interactions. J Chem Inf Model 53(3):592–600. PubMedCrossRefGoogle Scholar
  73. 73.
    Koppisetty CA, Frank M, Kemp GJ, Nyholm PG (2013) Computation of binding energies including their enthalpy and entropy components for protein–ligand complexes using support vector machines. J Chem Inf Model 53(10):2559–2570. PubMedCrossRefGoogle Scholar
  74. 74.
    Ding B, Li N, Wang W (2013) Characterizing binding of small molecules. II. Evaluating the potency of small molecules to combat resistance based on docking structures. J Chem Inf Model 53(5):1213–1222. PubMedCrossRefGoogle Scholar
  75. 75.
    Ding B, Wang J, Li N, Wang W (2013) Characterization of small molecule binding. I. Accurate identification of strong inhibitors in virtual screening. J Chem Inf Model 53(1):114–122. PubMedPubMedCentralCrossRefGoogle Scholar
  76. 76.
    Yan Y, Wang W, Sun Z, Zhang JZH, Ji C (2017) Protein–ligand empirical interaction components for virtual screening. J Chem Inf Model 57(8):1793–1806. PubMedCrossRefGoogle Scholar
  77. 77.
    Li H, Leung KS, Wong MH, Ballester PJ (2014) Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: cyscore as a case study. BMC Bioinf 15:291. CrossRefGoogle Scholar
  78. 78.
    Afifi K, Al-Sadek AF (2018) Improving classical scoring functions using random forest: the non-additivity of free energy terms’ contributions in binding. Chem Biol Drug Design. CrossRefGoogle Scholar
  79. 79.
    Wang C, Zhang Y (2017) Improving scoring-docking-screening powers of protein–ligand scoring functions using random forest. J Comput Chem 38(3):169–177. PubMedCrossRefGoogle Scholar
  80. 80.
    Zilian D, Sotriffer CA (2013) SFCscore(RF): a random forest-based scoring function for improved affinity prediction of protein–ligand complexes. J Chem Inf Model 53(8):1923–1933. PubMedCrossRefGoogle Scholar
  81. 81.
    Liu Q, Kwoh CK, Li J (2013) Binding affinity prediction for protein–ligand complexes based on beta contacts and B factor. J Chem Inf Model 53(11):3076–3085. PubMedCrossRefGoogle Scholar
  82. 82.
    Ballester PJ, Mitchell JBO (2010) A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Oxford University Press, OxfordCrossRefGoogle Scholar
  83. 83.
    Ballester PJ, Schreyer A, Blundell TL (2014) Does a more precise chemical description of protein–ligand complexes lead to more accurate prediction of binding affinity? J Chem Inf Model 54(3):944–955. PubMedPubMedCentralCrossRefGoogle Scholar
  84. 84.
    Li H, Leung KS, Wong MH, Ballester PJ (2015) Improving AutoDock Vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets. Mol Inf 34(2–3):115–126. CrossRefGoogle Scholar
  85. 85.
    Gabel J, Desaphy J, Rognan D (2014) Beware of machine learning-based scoring functions—on the danger of developing black boxes. J Chem Inf Model 54(10):2807–2815. PubMedCrossRefGoogle Scholar
  86. 86.
    Cang Z, Mu L, Wei GW (2018) Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLoS Comput Biol 14(1):e1005929. PubMedPubMedCentralCrossRefGoogle Scholar
  87. 87.
    Buiu C, Putz MV, Avram S (2016) Learning the relationship between the primary structure of HIV envelope glycoproteins and neutralization activity of particular antibodies by using artificial neural networks. Int J Mol Sci. PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Winkler DA, Burden FR (2007) Nonlinear predictive modeling of MHC class II-peptide binding using Bayesian neural networks. Methods Mol Biol (Clifton NJ) 409:365–377. CrossRefGoogle Scholar
  89. 89.
    Fabry-Asztalos L, Andonie R, Collar CJ, Abdul-Wahid S, Salim N (2008) A genetic algorithm optimized fuzzy neural network analysis of the affinity of inhibitors for HIV-1 protease. Bioorg Med Chem 16(6):2903–2911. PubMedCrossRefGoogle Scholar
  90. 90.
    Shen J, Cui Y, Gu J, Li Y, Li L (2014) A genetic algorithm-back propagation artificial neural network model to quantify the affinity of flavonoids toward P-glycoprotein. Combinatorial Chem High Throughput Screen 17(2):162–172CrossRefGoogle Scholar
  91. 91.
    O’Donnell TJ, Rubinsteyn A, Bonsack M, Riemer AB, Laserson U, Hammerbacher J (2018) MHCflurry: open-source class I MHC binding affinity prediction. Cell Syst 7(1):129–132.e124. PubMedCrossRefGoogle Scholar
  92. 92.
    Chupakhin V, Marcou G, Baskin I, Varnek A, Rognan D (2013) Predicting ligand binding modes from neural networks trained on protein–ligand interaction fingerprints. J Chem Inf Model 53(4):763–772. PubMedCrossRefGoogle Scholar
  93. 93.
    Durrant JD, Mccammon JA (2010) NNScore: a neural-network-based scoring function for the characterization of protein–ligand complexes. J Chem Inf Model 50(10):1865–1871PubMedPubMedCentralCrossRefGoogle Scholar
  94. 94.
    Durrant JD, McCammon JA (2011) NNScore 2.0: a neural-network receptor-ligand scoring function. J Chem Inf Model 51(11):2897–2903. PubMedPubMedCentralCrossRefGoogle Scholar
  95. 95.
    Durrant JD, Friedman AJ, Rogers KE, McCammon JA (2013) Comparing neural-network scoring functions and the state of the art: applications to common library screening. J Chem Inf Model 53(7):1726–1735. PubMedPubMedCentralCrossRefGoogle Scholar
  96. 96.
    Ashtawy HM, Mahapatra NR (2018) Boosted neural networks scoring functions for accurate ligand docking and ranking. J Bioinf Comput Biol 16(2):1850004. CrossRefGoogle Scholar
  97. 97.
    Ashtawy HM, Mahapatra NR (2015) BgN-Score and BsN-Score: bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein–ligand complexes. BMC Bioinf 16(Suppl 4):S8. CrossRefGoogle Scholar
  98. 98.
    Wallach I, Dzamba M, Heifets A (2015) AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery. Math Z 47(1):34–46Google Scholar
  99. 99.
    Ragoza M, Hochuli J, Idrobo E, Sunseri J, Koes DR (2017) Protein–ligand scoring with convolutional neural networks. J Chem Inf Model 57(4):942–957. PubMedPubMedCentralCrossRefGoogle Scholar
  100. 100.
    Gomes J, Ramsundar B, Feinberg EN, Pande VS (2017) Atomic convolutional networks for predicting protein–ligand binding affinity. arXiv preprint arXiv: 170310603Google Scholar
  101. 101.
    Stepniewska-Dziubinska MM, Zielenkiewicz P, Siedlecki P (2018) Development and evaluation of a deep learning model for protein–ligand binding affinity prediction. Bioinformatics. PubMedCentralCrossRefPubMedGoogle Scholar
  102. 102.
    Bengio Y, Vincent AC,P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):31Google Scholar
  103. 103.
    Meng ECSB, Kuntz ID (1992) Automated docking with grid-based energy evaluation. J Comput Chem 13:20CrossRefGoogle Scholar
  104. 104.
    Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3):727–748. PubMedCrossRefGoogle Scholar
  105. 105.
    Krammer A, Kirchhoff PD, Jiang X, Venkatachalam CM, Waldman M (2005) LigScore: a novel scoring function for predicting binding affinities. J Mol Graph Model 23(5):395–407. PubMedCrossRefGoogle Scholar
  106. 106.
    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749. PubMedCrossRefGoogle Scholar
  107. 107.
    Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (2015) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19(14):1639–1662CrossRefGoogle Scholar
  108. 108.
    Steinmann C, Olsson MA, Ryde U (2018) Relative ligand-binding free energies calculated from multiple short QM/MM MD simulations. J Chem Theory Comput 14(6):3228–3237. PubMedCrossRefGoogle Scholar
  109. 109.
    Chaskar P, Zoete V, Röhrig UF (2014) Toward on-the-fly quantum mechanical/molecular mechanical (QM/MM) docking: development and benchmark of a scoring function. J Chem Inf Model 54(11):3137–3152. PubMedCrossRefGoogle Scholar
  110. 110.
    Terp GE, Johansen BN, Christensen IT, Jorgensen FS (2001) A new concept for multidimensional selection of ligand conformations (MultiSelect) and multidimensional scoring (MultiScore) of protein–ligand binding affinities. J Med Chem 44(14):2333–2343PubMedCrossRefGoogle Scholar
  111. 111.
    Betzi S, Suhre K, Chetrit B, Guerlesquin F, Morelli X (2006) GFscore: a general nonlinear consensus scoring function for high-throughput docking. J Chem Inf Model 46(4):1704–1712. PubMedCrossRefGoogle Scholar
  112. 112.
    Bar-Haim S, Aharon A, Ben-Moshe T, Marantz Y, Senderowitz H (2009) SeleX-CS: a new consensus scoring algorithm for hit discovery and lead optimization. J Chem Inf Model 49(3):623–633. PubMedCrossRefGoogle Scholar
  113. 113.
    Plewczynski D, Lazniewski M, von Grotthuss M, Rychlewski L, Ginalski K (2011) VoteDock: consensus docking method for prediction of protein–ligand interactions. J Comput Chem 32(4):568–581. PubMedCrossRefGoogle Scholar
  114. 114.
    Zhang L, Liu Y, Wang M, Wu Z, Li N, Zhang J, Yang C (2017) EZH2-, CHD4-, and IDH-linked epigenetic perturbation and its association with survival in glioma patients. J Mol Cell Biol 9(6):477–488. PubMedCrossRefGoogle Scholar
  115. 115.
    Zhang L, Xiao M, Zhou J, Yu J (2018) Lineage-associated underrepresented permutations (LAUPs) of mammalian genomic sequences based on a Jellyfish-based LAUPs analysis application (JBLA). Bioinformatics 34(21):3624–3630. PubMedCrossRefGoogle Scholar
  116. 116.
    Santos-Martins D, Forli S, Ramos MJ, Olson AJ (2014) AutoDock4(Zn): an improved AutoDock force field for small-molecule docking to zinc metalloproteins. J Chem Inf Model 54(8):2371–2379. PubMedPubMedCentralCrossRefGoogle Scholar
  117. 117.
    Poli G, Jha V, Martinelli A, Supuran CT, Tuccinardi T (2018) Development of a fingerprint-based scoring function for the prediction of the binding mode of carbonic anhydrase II inhibitors. Int J Mol Sci. CrossRefPubMedPubMedCentralGoogle Scholar
  118. 118.
    Baek M, Shin WH, Chung HW, Seok C (2017) GalaxyDock BP2 score: a hybrid scoring function for accurate protein–ligand docking. J Comput Aided Mol Design 31(7):653–666. CrossRefGoogle Scholar
  119. 119.
    Shin WH, Kim JK, Kim DS, Seok C (2013) GalaxyDock2: protein–ligand docking using beta-complex and global optimization. J Comput Chem 34(30):2647–2656. PubMedCrossRefGoogle Scholar
  120. 120.
    Debroise T, Shakhnovich EI, Cheron N (2017) A hybrid knowledge-based and empirical scoring function for protein–ligand interaction: SMoG2016. J Chem Inf Model 57(3):584–593. PubMedCrossRefGoogle Scholar

Copyright information

© International Association of Scientists in the Interdisciplinary Areas 2019

Authors and Affiliations

  1. 1.College of Computer and Information ScienceSouthwest UniversityChongqingChina
  2. 2.School of Medical Information and EngineeringSouthwest Medical UniversityLuzhouChina
  3. 3.College of Pharmaceutical SciencesSouthwest UniversityChongqingChina
  4. 4.College of Computer ScienceSichuan UniversityChengduChina
  5. 5.Medical Big Data CenterSichuan UniversityChengduChina
  6. 6.Zdmedical, Information Polytron Technologies Inc ChongqingChongqingChina

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