Tính không cộng gộp trong dữ liệu công khai và dữ liệu nội bộ: những tác động đối với thiết kế thuốc
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Free SM, Wilson JW (1964) A mathematical contribution to structure-activity studies. J Med Chem 7:395–399
Hussain J, Rea C (2010) Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model 50:339–348
Patel Y, Gillet VJ, Howe T et al (2008) Assessment of additive/nonadditive effects in structure− activity relationships: implications for iterative drug design. J Med Chem 51:7552–7562
Wang L, Wu Y, Deng Y et al (2015) Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 137:2695–2703. https://doi.org/10.1021/ja512751q
Kramer C (2019) Nonadditivity Analysis. J Chem Inf Model 59:4034–4042. https://doi.org/10.1021/acs.jcim.9b00631
Dimova D, Heikamp K, Stumpfe D, Bajorath J (2013) Do medicinal chemists learn from activity cliffs? A systematic evaluation of cliff progression in evolving compound data sets. J Med Chem 56:3339–3345
Mobley DL, Gilson MK (2017) Predicting binding free energies: frontiers and benchmarks. Annu Rev Biophys 46:531–558
Hu H, Bajorath J (2020) Introducing a new category of activity cliffs combining different compound similarity criteria. RSC Med Chem. 11(1):132–41
Abramyan TM, An Y, Kireev D (2019) Off-pocket activity cliffs: a puzzling facet of molecular recognition. J Chem Inf Model. 60(1):152–61
Andrews SP, Mason JS, Hurrell E, Congreve M (2014) Structure-based drug design of chromone antagonists of the adenosine A2A receptor. Medchemcomm 5:571–575. https://doi.org/10.1039/C3MD00338H
Schönherr H, Cernak T (2013) Profound methyl effects in drug discovery and a call for new C-H methylation reactions. Angew Chemie Int Ed 52:12256–12267
Kramer C, Fuchs JE, Liedl KR (2015) Strong nonadditivity as a key structure-activity relationship feature: distinguishing structural changes from assay artifacts. J Chem Inf Model 55:483–494. https://doi.org/10.1021/acs.jcim.5b00018
Gomez L, Xu R, Sinko W et al (2018) Mathematical and structural characterization of strong nonadditive structure-activity relationship caused by protein conformational changes. J Med Chem 61:7754–7766
Baum B, Muley L, Smolinski M et al (2010) Non-additivity of functional group contributions in protein–ligand binding: a comprehensive study by crystallography and isothermal titration calorimetry. J Mol Biol 397:1042–1054
McClure K, Hack M, Huang L et al (2006) Pyrazole CCK1 receptor antagonists. Part 1: Solution-phase library synthesis and determination of Free-Wilson additivity. Bioorg Med Chem Lett 16:72–76
Sehon C, McClure K, Hack M et al (2006) Pyrazole CCK1 receptor antagonists. Part 2: SAR studies by solid-phase library synthesis and determination of Free-Wilson additivity. Bioorg Med Chem Lett 16:77–80
Hilpert K, Ackermann J, Banner DW et al (2002) Design and synthesis of potent and highly selective thrombin inhibitors. J Med Chem 37:3889–3901
Lübbers T, Böhringer M, Gobbi L et al (2007) 1, 3-disubstituted 4-aminopiperidines as useful tools in the optimization of the 2-aminobenzo [a] quinolizine dipeptidyl peptidase IV inhibitors. Bioorg Med Chem Lett 17:2966–2970
Leung CS, Leung SSF, Tirado-Rives J, Jorgensen WL (2012) Methyl effects on protein–ligand binding. J Med Chem 55:4489–4500
Abeliovich H (2005) An empirical extremum principle for the hill coefficient in ligand–protein interactions showing negative cooperativity. Biophys J 89:76–79
Camara-Campos A, Musumeci D, Hunter CA, Turega S (2009) Chemical double mutant cycles for the quantification of cooperativity in H-bonded complexes. J Am Chem Soc 131:18518–18524
Cockroft SL, Hunter CA (2007) Chemical double-mutant cycles: dissecting non-covalent interactions. Chem Soc Rev 36:172–188
Babaoglu K, Shoichet BK (2006) Deconstructing fragment-based inhibitor discovery. Nat Chem Biol 2:720–723
Miller BG, Wolfenden R (2002) Catalytic proficiency: the unusual case of OMP decarboxylase. Annu Rev Biochem 71:847–885
Hajduk PJ, Sheppard G, Nettesheim DG et al (1997) Discovery of potent nonpeptide inhibitors of stromelysin using SAR by NMR. J Am Chem Soc 119:5818–5827
Congreve MS, Davis DJ, Devine L et al (2003) Detection of ligands from a dynamic combinatorial library by X-ray crystallography. Angew Chemie Int Ed 42:4479–4482
Sharrow SD, Edmonds KA, Goodman MA et al (2005) Thermodynamic consequences of disrupting a water-mediated hydrogen bond network in a protein: pheromone complex. Protein Sci 14:249–256
Muley L, Baum B, Smolinski M et al (2010) Enhancement of hydrophobic interactions and hydrogen bond strength by cooperativity: synthesis, modeling, and molecular dynamics simulations of a congeneric series of thrombin inhibitors. J Med Chem 53:2126–2135
Kuhn B, Mohr P, Stahl M (2010) Intramolecular hydrogen bonding in medicinal chemistry. J Med Chem 53:2601–2611. https://doi.org/10.1021/jm100087s
Kramer C, Kalliokoski T, Gedeck P, Vulpetti A (2012) The experimental uncertainty of heterogeneous public K i data. J Med Chem 55:5165–5173. https://doi.org/10.1021/jm300131x
Kalliokoski T, Kramer C, Vulpetti A, Gedeck P (2013) Comparability of mixed IC50 data–a statistical analysis. PLoS ONE 8:e61007
Kramer C, Dahl G, Tyrchan C, Ulander J (2016) A comprehensive company database analysis of biological assay variability. Drug Discov. Today 21:1213–1221
Segler MHS, Kogej T, Tyrchan C, Waller MP (2018) Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci 4:120–131. https://doi.org/10.1021/acscentsci.7b00512
Arús-Pous J, Blaschke T, Ulander S et al (2019) Exploring the GDB-13 chemical space using deep generative models. J Cheminform 11:20. https://doi.org/10.1186/s13321-019-0341-z
Blaschke T, Arús-Pous J, Chen H et al (2020) REINVENT 2.0 – an AI tool for de novo drug design. J Chem Inf Model. https://doi.org/10.26434/CHEMRXIV.12058026.V2
Olivecrona M, Blaschke T, Engkvist O, Chen H (2017) Molecular de-novo design through deep reinforcement learning. J Cheminform 9:48. https://doi.org/10.1186/s13321-017-0235-x
Stepniewska-Dziubinska MM, Zielenkiewicz P, Siedlecki P (2018) Development and evaluation of a deep learning model for protein–ligand binding affinity prediction. Bioinformatics 34:3666–3674. https://doi.org/10.1093/bioinformatics/bty374
Gomes J, Ramsundar B, Feinberg EN, Pande VS (2017) Atomic convolutional networks for predicting protein-ligand binding affinity. arXiv:1703.10603
Feinberg EN, Sur D, Wu Z et al (2018) PotentialNet for Molecular Property Prediction. ACS Cent Sci 4:1520–1530. https://doi.org/10.1021/acscentsci.8b00507
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 Inf Model 58:287–296. https://doi.org/10.1021/acs.jcim.7b00650
Wójcikowski M, Ballester PJ, Siedlecki P (2017) Performance of machine-learning scoring functions in structure-based virtual screening. Sci Rep 7:1–10. https://doi.org/10.1038/srep46710
Ragoza M, Hochuli J, Idrobo E et al (2017) Protein-ligand scoring with convolutional neural networks. J Chem Inf Model 57:942–957. https://doi.org/10.1021/acs.jcim.6b00740
Pereira JC, Caffarena ER, Dos Santos CN (2016) Boosting docking-based virtual screening with deep learning. J Chem Inf Model 56:2495–2506. https://doi.org/10.1021/acs.jcim.6b00355
Wallach I, Dzamba M, Heifets A (2015) AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery. arXiv:1510.02855
Ballester PJ, Mitchell JBO (2010) A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 26:1169–1175. https://doi.org/10.1093/bioinformatics/btq112
Kayala MA, Baldi P (2012) ReactionPredictor: Prediction of complex chemical reactions at the mechanistic level using machine learning. J Chem Inf Model 52:2526–2540. https://doi.org/10.1021/ci3003039
Struble TJ, Alvarez JC, Brown SP et al (2020) Current and future roles of artificial intelligence in medicinal chemistry synthesis. J Med Chem. https://doi.org/10.1021/acs.jmedchem.9b02120
Segler MHS, Waller MP (2017) Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chem - A Eur J 23:5966–5971. https://doi.org/10.1002/chem.201605499
Schwaller P, Gaudin T, Lányi D et al (2018) “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models. Chem Sci 9:6091–6098. https://doi.org/10.1039/c8sc02339e
Sheridan RP, Karnachi P, Tudor M et al (2020) Experimental error, kurtosis, activity cliffs, and methodology: what limits the predictivity of quantitative structure-activity relationship models? J Chem Inf Model 60:1969–1982. https://doi.org/10.1021/acs.jcim.9b01067
RDKit: Open-Source Cheminformatics Software. https://www.rdkit.org
Dalke A, Hert J, Kramer C (2018) mmpdb: an open-source matched molecular pair platform for large multiproperty data sets. J Chem Inf Model 58:902–910. https://doi.org/10.1021/acs.jcim.8b00173
Gaulton A, Hersey A, Nowotka M et al (2017) The ChEMBL database in 2017. Nucleic Acids Res 45:D945–D954
Akiba T, Sano S, Yanase T et al (2019) Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, USA, pp 2623–2631. https://doi.org/10.1145/3292500.3330701
Sarica A, Cerasa A, Quattrone A (2017) Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: a systematic review. Front Aging Neurosci 9:329. https://doi.org/10.3389/fnagi.2017.00329
Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21:6. https://doi.org/10.1186/s12864-019-6413-7
Kolmogorov AN (1933) Sulla determinazione empírica di uma legge di distribuzione (On the empirical determination of a distribution law). Giorn Ist Ital Attuar 4:83–91
Smirnov N (1948) Table for estimating the goodness of fit of empirical distributions. Ann Math Stat 19:279–281
Kruskal WH, Wallis WA (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47:583–621
Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18(1):50–60. https://doi.org/10.1214/aoms/1177730491
