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Dokowanie molekularne (Polish Wikipedia)

Analysis of information sources in references of the Wikipedia article "Dokowanie molekularne" in Polish language version.

refsWebsite
Global rank Polish rank
54doi.org
2nd place
6th place
45worldcat.org
5th place
2nd place
2nih.gov
4th place
7th place
1sciencedirect.com
149th place
235th place
1aspetjournals.org
6,663rd place
7,639th place
1acs.org
1,248th place
3,275th place

acs.org

pubs.acs.org

aspetjournals.org

pharmrev.aspetjournals.org

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dx.doi.org

  • Francesca Stanzione, Ilenia Giangreco, Jason C. Cole, Chapter Four – Use of molecular docking computational tools in drug discovery, David R. Witty, Brian Cox (red.), t. 60, Elsevier, 2021, s. 273–343, DOI: 10.1016/bs.pmch.2021.01.004 [dostęp 2022-01-16] (ang.).
  • Gerard Pujadas i inni, Protein-ligand Docking: A Review of Recent Advances and Future Perspectives, „Current Pharmaceutical Analysis”, 4 (1), 2008, s. 1–19, DOI: 10.2174/157341208783497597, ISSN 1573-4129 [dostęp 2022-01-16].
  • Gregory Sliwoski i inni, Computational Methods in Drug Discovery, „Pharmacological Reviews”, 66 (1), 2014, s. 334–395, DOI: 10.1124/pr.112.007336, ISSN 0031-6997, PMID: 24381236, PMCID: PMC3880464 [dostęp 2022-01-16] (ang.).
  • Che-Lun Hung, Chi-Chun Chen, Computational Approaches for Drug Discovery, „Drug Development Research”, 75 (6), 2014, s. 412–418, DOI: 10.1002/ddr.21222, ISSN 0272-4391 [dostęp 2022-01-16].
  • Daniel E. Koshland, The Key–Lock Theory and the Induced Fit Theory, „Angewandte Chemie International Edition in English”, 33 (2324), 1995, s. 2375–2378, DOI: 10.1002/anie.199423751, ISSN 0570-0833 [dostęp 2022-01-16].
  • Xuan-Yu Meng i inni, Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery, „Current Computer Aided-Drug Design”, 7 (2), 2011, s. 146–157, DOI: 10.2174/157340911795677602, ISSN 1573-4099 [dostęp 2022-01-16].
  • Sheng-You Huang, Xiaoqin Zou, Advances and Challenges in Protein-Ligand Docking, „International Journal of Molecular Sciences”, 11 (8), 2010, s. 3016–3034, DOI: 10.3390/ijms11083016, ISSN 1422-0067 [dostęp 2022-01-16].
  • Stefano Forli, Arthur J. Olson, A Force Field with Discrete Displaceable Waters and Desolvation Entropy for Hydrated Ligand Docking, „Journal of Medicinal Chemistry”, 55 (2), 2012, s. 623–638, DOI: 10.1021/jm2005145, ISSN 0022-2623 [dostęp 2022-01-16].
  • Julien Michel, Julian Tirado-Rives, William L. Jorgensen, Prediction of the Water Content in Protein Binding Sites, „The Journal of Physical Chemistry B”, 113 (40), 2009, s. 13337–13346, DOI: 10.1021/jp9047456, ISSN 1520-6106 [dostęp 2022-01-16] (ang.).
  • Marley L. Samways i inni, Water molecules at protein–drug interfaces: computational prediction and analysis methods, „Chemical Society Reviews”, 50 (16), 2021, s. 9104–9120, DOI: 10.1039/d0cs00151a, ISSN 0306-0012 [dostęp 2022-01-16].
  • Ligand-Protein Docking with Water Molecules, DOI: 10.1021/ci700285e.s002 [dostęp 2022-01-16].
  • Prediction of Ordered Water Molecules in Protein Binding Sites from Molecular Dynamics Simulations: The Impact of Ligand Binding on Hydration Networks, DOI: 10.1021/acs.jcim.7b00520.s001 [dostęp 2022-01-16].
  • Gregory A. Ross, Garrett M. Morris, Philip C. Biggin, Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites, „PLoS ONE”, 7 (3), 2012, e32036, DOI: 10.1371/journal.pone.0032036, ISSN 1932-6203 [dostęp 2022-01-16].
  • Alessio Amadasi i inni, Robust Classification of “Relevant” Water Molecules in Putative Protein Binding Sites, „Journal of Medicinal Chemistry”, 51 (4), 2008, s. 1063–1067, DOI: 10.1021/jm701023h, ISSN 0022-2623 [dostęp 2022-01-16].
  • Maxim Totrov, Ruben Abagyan, Flexible ligand docking to multiple receptor conformations: a practical alternative, „Current Opinion in Structural Biology”, 18 (2), 2008, s. 178–184, DOI: 10.1016/j.sbi.2008.01.004, ISSN 0959-440X [dostęp 2022-01-16].
  • Chandrika B-Rao, Jyothi Subramanian, Somesh D. Sharma, Managing protein flexibility in docking and its applications, „Drug Discovery Today”, 14 (7–8), 2009, s. 394–400, DOI: 10.1016/j.drudis.2009.01.003, ISSN 1359-6446 [dostęp 2022-01-16].
  • Pietro Cozzini, ChemInform Abstract: Target Flexibility: An Emerging Consideration in Drug Discovery and Design, „ChemInform”, 40 (3), 2009, DOI: 10.1002/chin.200903235, ISSN 0931-7597 [dostęp 2022-01-16].
  • Rommie E. Amaro i inni, Ensemble Docking in Drug Discovery, „Biophysical Journal”, 114 (10), 2018, s. 2271–2278, DOI: 10.1016/j.bpj.2018.02.038, ISSN 0006-3495, PMID: 29606412, PMCID: PMC6129458 [dostęp 2022-01-16].
  • Sheng-You Huang, Xiaoqin Zou, Ensemble docking of multiple protein structures: Considering protein structural variations in molecular docking, „Proteins: Structure, Function, and Bioinformatics”, 66 (2), 2006, s. 399–421, DOI: 10.1002/prot.21214, ISSN 0887-3585 [dostęp 2022-01-16].
  • Janusz Bujnicki, Faculty Opinions recommendation of Highly accurate protein structure prediction with AlphaFold., „Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature”, 2021, DOI: 10.3410/f.740477161.793587947 [dostęp 2022-01-16].
  • Janusz Bujnicki, Faculty Opinions recommendation of Highly accurate protein structure prediction for the human proteome., „Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature”, 2021, DOI: 10.3410/f.740510745.793587948 [dostęp 2022-01-16].
  • What is Density Functional Theory?, Hoboken, NJ, USA: John Wiley & Sons, Inc., s. 1–33, DOI: 10.1002/9780470447710.ch1 [dostęp 2022-01-16].
  • Stephen J Campbell i inni, Ligand binding: functional site location, similarity and docking, „Current Opinion in Structural Biology”, 13 (3), 2003, s. 389–395, DOI: 10.1016/s0959-440x(03)00075-7, ISSN 0959-440X [dostęp 2022-01-16].
  • Zhong-Ru Xie, Ming-Jing Hwang, Methods for Predicting Protein–Ligand Binding Sites, New York, NY: Springer New York, 3 września 2014, s. 383–398, DOI: 10.1007/978-1-4939-1465-4_17, ISBN 978-1-4939-1464-7 [dostęp 2022-01-16].
  • Yang Liu i inni, CB-Dock: a web server for cavity detection-guided protein–ligand blind docking, „Acta Pharmacologica Sinica”, 41 (1), 2019, s. 138–144, DOI: 10.1038/s41401-019-0228-6, ISSN 1671-4083 [dostęp 2022-01-16].
  • Mark N. Wass, Lawrence A. Kelley, Michael J.E. Sternberg, 3DLigandSite: predicting ligand-binding sites using similar structures, „Nucleic Acids Research”, 38 (suppl_2), 2010, W469–W473, DOI: 10.1093/nar/gkq406, ISSN 1362-4962 [dostęp 2022-01-16].
  • Jonathan C. Fuller, Nicholas J. Burgoyne, Richard M. Jackson, Predicting druggable binding sites at the protein–protein interface, „Drug Discovery Today”, 14 (3–4), 2009, s. 155–161, DOI: 10.1016/j.drudis.2008.10.009, ISSN 1359-6446 [dostęp 2022-01-16].
  • B. Sandak, Efficient Computational Algorithms for Fast Electrostatics and Molecular Docking, Berlin, Heidelberg: Springer Berlin Heidelberg, 2002, s. 411–441, DOI: 10.1007/978-3-642-56080-4_17, ISBN 978-3-540-43756-7 [dostęp 2022-01-16].
  • Hitoshi Goto, Eiji Osawa, Corner flapping: a simple and fast algorithm for exhaustive generation of ring conformations, „Journal of the American Chemical Society”, 111 (24), 1989, s. 8950–8951, DOI: 10.1021/ja00206a046, ISSN 0002-7863 [dostęp 2022-01-16].
  • R. Rohs, Molecular flexibility in ab initio drug docking to DNA: binding-site and binding-mode transitions in all-atom Monte Carlo simulations, „Nucleic Acids Research”, 33 (22), 2005, s. 7048–7057, DOI: 10.1093/nar/gki1008, ISSN 0305-1048 [dostęp 2022-01-16].
  • Oliver Korb, Thomas Stützle, Thomas E. Exner, PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design, Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, s. 247–258, DOI: 10.1007/11839088_22, ISBN 978-3-540-38482-3 [dostęp 2022-01-16].
  • Jin Li, Ailing Fu, Le Zhang, An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking, „Interdisciplinary Sciences: Computational Life Sciences”, 11 (2), 2019, s. 320–328, DOI: 10.1007/s12539-019-00327-w, ISSN 1913-2751 [dostęp 2022-01-16].
  • Renxiao Wang, Yipin Lu, Shaomeng Wang, Comparative Evaluation of 11 Scoring Functions for Molecular Docking, „Journal of Medicinal Chemistry”, 46 (12), 2003, s. 2287–2303, DOI: 10.1021/jm0203783, ISSN 0022-2623 [dostęp 2022-01-16].
  • Maciej Wójcikowski, Pawel Siedlecki, Pedro J. Ballester, Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity, New York, NY: Springer New York, 2019, s. 1–12, DOI: 10.1007/978-1-4939-9752-7_1, ISBN 978-1-4939-9751-0 [dostęp 2022-01-16].
  • Alexander D. Mackerell, Empirical force fields for biological macromolecules: Overview and issues, „Journal of Computational Chemistry”, 25 (13), 2004, s. 1584–1604, DOI: 10.1002/jcc.20082, ISSN 0192-8651 [dostęp 2022-01-16].
  • Jay W. Ponder, David A. Case, Force Fields for Protein Simulations, Elsevier, 2003, s. 27–85, DOI: 10.1016/s0065-3233(03)66002-x [dostęp 2022-01-16].
  • Chao Shen i inni, From machine learning to deep learning: Advances in scoring functions for protein–ligand docking, „WIREs Computational Molecular Science”, 10 (1), 2019, DOI: 10.1002/wcms.1429, ISSN 1759-0876 [dostęp 2022-01-16].
  • Hongjian Li i inni, Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets, „Molecular Informatics”, 34 (2–3), 2015, s. 115–126, DOI: 10.1002/minf.201400132, ISSN 1868-1743 [dostęp 2022-01-16].
  • Jacob D. Durrant, J. Andrew McCammon, NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein–Ligand Complexes, „Journal of Chemical Information and Modeling”, 50 (10), 2010, s. 1865–1871, DOI: 10.1021/ci100244v, ISSN 1549-9596 [dostęp 2022-01-16].
  • Yu-Chian Chen, Erratum: Beware of Docking!, „Trends in Pharmacological Sciences”, 36 (9), 2015, s. 617, DOI: 10.1016/j.tips.2015.01.004, ISSN 0165-6147 [dostęp 2022-01-16].
  • Cosolvent and Dynamic Effects in Binding Pocket Search by Docking Simulations, DOI: 10.1021/acs.jcim.1c00924.s001 [dostęp 2022-01-16].
  • Nataraj S. Pagadala, Khajamohiddin Syed, Jack Tuszynski, Software for molecular docking: a review, „Biophysical Reviews”, 9 (2), 2017, s. 91–102, DOI: 10.1007/s12551-016-0247-1, ISSN 1867-2450 [dostęp 2022-01-16].
  • Eric D. Boittier i inni, Assessing Molecular Docking Tools to Guide Targeted Drug Discovery of CD38 Inhibitors, „International Journal of Molecular Sciences”, 21 (15), 2020, s. 5183, DOI: 10.3390/ijms21155183, ISSN 1422-0067 [dostęp 2022-01-16].
  • Juan Pablo Arcon i inni, AutoDock Bias: improving binding mode prediction and virtual screening using known protein–ligand interactions, „Bioinformatics”, 35 (19), 2019, s. 3836–3838, DOI: 10.1093/bioinformatics/btz152, ISSN 1367-4803 [dostęp 2022-01-16].
  • Leonardo Solis-Vasquez i inni, Benchmarking the performance of irregular computations in AutoDock-GPU molecular docking, „Parallel Computing”, 109, 2022, s. 102861, DOI: 10.1016/j.parco.2021.102861, ISSN 0167-8191 [dostęp 2022-01-16].
  • Xuben Hou i inni, How to Improve Docking Accuracy of AutoDock4.2: A Case Study Using Different Electrostatic Potentials, „Journal of Chemical Information and Modeling”, 53 (1), 2013, s. 188–200, DOI: 10.1021/ci300417y, ISSN 1549-9596 [dostęp 2022-01-16].
  • Oleg Trott, Arthur J. Olson, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, „Journal of Computational Chemistry”, 2009, NA–NA, DOI: 10.1002/jcc.21334, ISSN 0192-8651 [dostęp 2022-01-16].
  • Vsevolod Tanchuk i inni, A New Scoring Function for Molecular Docking Based on AutoDock and AutoDock Vina, „Current Drug Discovery Technologies”, 12 (3), 2015, s. 170–178, DOI: 10.2174/1570163812666150825110208, ISSN 1570-1638 [dostęp 2022-01-16].
  • Demetri T. Moustakas i inni, Development and validation of a modular, extensible docking program: DOCK 5, „Journal of Computer-Aided Molecular Design”, 20 (10–11), 2006, s. 601–619, DOI: 10.1007/s10822-006-9060-4, ISSN 0920-654X [dostęp 2022-01-16].
  • Glide: A New Approach for Rapid, Accurate Docking and Scoring. I. Method and Assessment of Docking Accuracy, DOI: 10.1021/jm0306430.s001 [dostęp 2022-01-16].
  • Marcel L. Verdonk i inni, Improved protein-ligand docking using GOLD, „Proteins: Structure, Function, and Bioinformatics”, 52 (4), 2003, s. 609–623, DOI: 10.1002/prot.10465, ISSN 0887-3585 [dostęp 2022-01-16].
  • Bernd Kramer, Matthias Rarey, Thomas Lengauer, Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking, „Proteins: Structure, Function, and Genetics”, 37 (2), 1999, s. 228–241, DOI: 10.1002/(sici)1097-0134(19991101)37:2<228::aid-prot8>3.0.co;2-8, ISSN 0887-3585 [dostęp 2022-01-16].
  • Veronica Salmaso, Stefano Moro, Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview, „Frontiers in Pharmacology”, 9, 2018, DOI: 10.3389/fphar.2018.00923, ISSN 1663-9812 [dostęp 2022-01-16].
  • Hernan Alonso, Andrey A. Bliznyuk, Jill E. Gready, Combining Docking and Molecular Dynamic Simulations in Drug Design, „ChemInform”, 37 (45), 2006, DOI: 10.1002/chin.200645280, ISSN 0931-7597 [dostęp 2022-01-16].

nih.gov

ncbi.nlm.nih.gov

sciencedirect.com

worldcat.org

  • Gerard Pujadas i inni, Protein-ligand Docking: A Review of Recent Advances and Future Perspectives, „Current Pharmaceutical Analysis”, 4 (1), 2008, s. 1–19, DOI: 10.2174/157341208783497597, ISSN 1573-4129 [dostęp 2022-01-16].
  • Gregory Sliwoski i inni, Computational Methods in Drug Discovery, „Pharmacological Reviews”, 66 (1), 2014, s. 334–395, DOI: 10.1124/pr.112.007336, ISSN 0031-6997, PMID: 24381236, PMCID: PMC3880464 [dostęp 2022-01-16] (ang.).
  • Che-Lun Hung, Chi-Chun Chen, Computational Approaches for Drug Discovery, „Drug Development Research”, 75 (6), 2014, s. 412–418, DOI: 10.1002/ddr.21222, ISSN 0272-4391 [dostęp 2022-01-16].
  • Daniel E. Koshland, The Key–Lock Theory and the Induced Fit Theory, „Angewandte Chemie International Edition in English”, 33 (2324), 1995, s. 2375–2378, DOI: 10.1002/anie.199423751, ISSN 0570-0833 [dostęp 2022-01-16].
  • Xuan-Yu Meng i inni, Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery, „Current Computer Aided-Drug Design”, 7 (2), 2011, s. 146–157, DOI: 10.2174/157340911795677602, ISSN 1573-4099 [dostęp 2022-01-16].
  • Sheng-You Huang, Xiaoqin Zou, Advances and Challenges in Protein-Ligand Docking, „International Journal of Molecular Sciences”, 11 (8), 2010, s. 3016–3034, DOI: 10.3390/ijms11083016, ISSN 1422-0067 [dostęp 2022-01-16].
  • Stefano Forli, Arthur J. Olson, A Force Field with Discrete Displaceable Waters and Desolvation Entropy for Hydrated Ligand Docking, „Journal of Medicinal Chemistry”, 55 (2), 2012, s. 623–638, DOI: 10.1021/jm2005145, ISSN 0022-2623 [dostęp 2022-01-16].
  • Julien Michel, Julian Tirado-Rives, William L. Jorgensen, Prediction of the Water Content in Protein Binding Sites, „The Journal of Physical Chemistry B”, 113 (40), 2009, s. 13337–13346, DOI: 10.1021/jp9047456, ISSN 1520-6106 [dostęp 2022-01-16] (ang.).
  • Marley L. Samways i inni, Water molecules at protein–drug interfaces: computational prediction and analysis methods, „Chemical Society Reviews”, 50 (16), 2021, s. 9104–9120, DOI: 10.1039/d0cs00151a, ISSN 0306-0012 [dostęp 2022-01-16].
  • Gregory A. Ross, Garrett M. Morris, Philip C. Biggin, Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites, „PLoS ONE”, 7 (3), 2012, e32036, DOI: 10.1371/journal.pone.0032036, ISSN 1932-6203 [dostęp 2022-01-16].
  • Alessio Amadasi i inni, Robust Classification of “Relevant” Water Molecules in Putative Protein Binding Sites, „Journal of Medicinal Chemistry”, 51 (4), 2008, s. 1063–1067, DOI: 10.1021/jm701023h, ISSN 0022-2623 [dostęp 2022-01-16].
  • Maxim Totrov, Ruben Abagyan, Flexible ligand docking to multiple receptor conformations: a practical alternative, „Current Opinion in Structural Biology”, 18 (2), 2008, s. 178–184, DOI: 10.1016/j.sbi.2008.01.004, ISSN 0959-440X [dostęp 2022-01-16].
  • Chandrika B-Rao, Jyothi Subramanian, Somesh D. Sharma, Managing protein flexibility in docking and its applications, „Drug Discovery Today”, 14 (7–8), 2009, s. 394–400, DOI: 10.1016/j.drudis.2009.01.003, ISSN 1359-6446 [dostęp 2022-01-16].
  • Pietro Cozzini, ChemInform Abstract: Target Flexibility: An Emerging Consideration in Drug Discovery and Design, „ChemInform”, 40 (3), 2009, DOI: 10.1002/chin.200903235, ISSN 0931-7597 [dostęp 2022-01-16].
  • Rommie E. Amaro i inni, Ensemble Docking in Drug Discovery, „Biophysical Journal”, 114 (10), 2018, s. 2271–2278, DOI: 10.1016/j.bpj.2018.02.038, ISSN 0006-3495, PMID: 29606412, PMCID: PMC6129458 [dostęp 2022-01-16].
  • Sheng-You Huang, Xiaoqin Zou, Ensemble docking of multiple protein structures: Considering protein structural variations in molecular docking, „Proteins: Structure, Function, and Bioinformatics”, 66 (2), 2006, s. 399–421, DOI: 10.1002/prot.21214, ISSN 0887-3585 [dostęp 2022-01-16].
  • Steven M. Bachrach, Computational organic chemistry, Wiley-Interscience, 2007, ISBN 978-0-470-14812-9, OCLC 180192820 [dostęp 2022-01-16].
  • Stephen J Campbell i inni, Ligand binding: functional site location, similarity and docking, „Current Opinion in Structural Biology”, 13 (3), 2003, s. 389–395, DOI: 10.1016/s0959-440x(03)00075-7, ISSN 0959-440X [dostęp 2022-01-16].
  • Yang Liu i inni, CB-Dock: a web server for cavity detection-guided protein–ligand blind docking, „Acta Pharmacologica Sinica”, 41 (1), 2019, s. 138–144, DOI: 10.1038/s41401-019-0228-6, ISSN 1671-4083 [dostęp 2022-01-16].
  • Mark N. Wass, Lawrence A. Kelley, Michael J.E. Sternberg, 3DLigandSite: predicting ligand-binding sites using similar structures, „Nucleic Acids Research”, 38 (suppl_2), 2010, W469–W473, DOI: 10.1093/nar/gkq406, ISSN 1362-4962 [dostęp 2022-01-16].
  • Jonathan C. Fuller, Nicholas J. Burgoyne, Richard M. Jackson, Predicting druggable binding sites at the protein–protein interface, „Drug Discovery Today”, 14 (3–4), 2009, s. 155–161, DOI: 10.1016/j.drudis.2008.10.009, ISSN 1359-6446 [dostęp 2022-01-16].
  • Hitoshi Goto, Eiji Osawa, Corner flapping: a simple and fast algorithm for exhaustive generation of ring conformations, „Journal of the American Chemical Society”, 111 (24), 1989, s. 8950–8951, DOI: 10.1021/ja00206a046, ISSN 0002-7863 [dostęp 2022-01-16].
  • Michael D. Vose, Simple genetic algorithm. Foundations and theory, [publisher not identified], ISBN 978-0-262-28564-3, OCLC 956674574 [dostęp 2022-01-16].
  • R. Rohs, Molecular flexibility in ab initio drug docking to DNA: binding-site and binding-mode transitions in all-atom Monte Carlo simulations, „Nucleic Acids Research”, 33 (22), 2005, s. 7048–7057, DOI: 10.1093/nar/gki1008, ISSN 0305-1048 [dostęp 2022-01-16].
  • Jin Li, Ailing Fu, Le Zhang, An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking, „Interdisciplinary Sciences: Computational Life Sciences”, 11 (2), 2019, s. 320–328, DOI: 10.1007/s12539-019-00327-w, ISSN 1913-2751 [dostęp 2022-01-16].
  • Bruce R. Donald, Algorithms in structural molecular biology, MIT Press, 2011, ISBN 978-0-262-01559-2, OCLC 667592553 [dostęp 2022-01-16].
  • Renxiao Wang, Yipin Lu, Shaomeng Wang, Comparative Evaluation of 11 Scoring Functions for Molecular Docking, „Journal of Medicinal Chemistry”, 46 (12), 2003, s. 2287–2303, DOI: 10.1021/jm0203783, ISSN 0022-2623 [dostęp 2022-01-16].
  • Alexander D. Mackerell, Empirical force fields for biological macromolecules: Overview and issues, „Journal of Computational Chemistry”, 25 (13), 2004, s. 1584–1604, DOI: 10.1002/jcc.20082, ISSN 0192-8651 [dostęp 2022-01-16].
  • Chao Shen i inni, From machine learning to deep learning: Advances in scoring functions for protein–ligand docking, „WIREs Computational Molecular Science”, 10 (1), 2019, DOI: 10.1002/wcms.1429, ISSN 1759-0876 [dostęp 2022-01-16].
  • Jones i inni, Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference, 17 maja 2020, OCLC 1228408141 [dostęp 2022-01-16].
  • Hongjian Li i inni, Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets, „Molecular Informatics”, 34 (2–3), 2015, s. 115–126, DOI: 10.1002/minf.201400132, ISSN 1868-1743 [dostęp 2022-01-16].
  • Jacob D. Durrant, J. Andrew McCammon, NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein–Ligand Complexes, „Journal of Chemical Information and Modeling”, 50 (10), 2010, s. 1865–1871, DOI: 10.1021/ci100244v, ISSN 1549-9596 [dostęp 2022-01-16].
  • Yu-Chian Chen, Erratum: Beware of Docking!, „Trends in Pharmacological Sciences”, 36 (9), 2015, s. 617, DOI: 10.1016/j.tips.2015.01.004, ISSN 0165-6147 [dostęp 2022-01-16].
  • Nataraj S. Pagadala, Khajamohiddin Syed, Jack Tuszynski, Software for molecular docking: a review, „Biophysical Reviews”, 9 (2), 2017, s. 91–102, DOI: 10.1007/s12551-016-0247-1, ISSN 1867-2450 [dostęp 2022-01-16].
  • Eric D. Boittier i inni, Assessing Molecular Docking Tools to Guide Targeted Drug Discovery of CD38 Inhibitors, „International Journal of Molecular Sciences”, 21 (15), 2020, s. 5183, DOI: 10.3390/ijms21155183, ISSN 1422-0067 [dostęp 2022-01-16].
  • Juan Pablo Arcon i inni, AutoDock Bias: improving binding mode prediction and virtual screening using known protein–ligand interactions, „Bioinformatics”, 35 (19), 2019, s. 3836–3838, DOI: 10.1093/bioinformatics/btz152, ISSN 1367-4803 [dostęp 2022-01-16].
  • Leonardo Solis-Vasquez i inni, Benchmarking the performance of irregular computations in AutoDock-GPU molecular docking, „Parallel Computing”, 109, 2022, s. 102861, DOI: 10.1016/j.parco.2021.102861, ISSN 0167-8191 [dostęp 2022-01-16].
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