Autoqsar: An Automated Machine Learning Tool for Best-Practice Quantitative Structure–Activity Relationship Modeling

References

• Li J , BallmerSG, GillisEPet al. Synthesis of many different types of organic small molecules using one automated process. Science347 (6227), 1221–1226 (2015).
   PubMed Web of Science ®Google Scholar
• Szymkuć S , GajewskaEP, KlucznikTet al. Computer-assisted synthetic planning: the end of the beginning. Angew. Chem. Int. Ed. Engl.55 (20), 5904–5937 (2016).
   PubMed Web of Science ®Google Scholar
• Wang L , WuY, DengYet al. 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 (7), 2695–2703 (2015).
   PubMed Web of Science ®Google Scholar
• Yazawa H , HirasawaA, HorieKet al. Oxytocin receptors expressed and coupled to Ca2+ signalling in a human vascular smooth muscle cell line. Br. J. Pharmacol.117 (5), 799–804 (1996).
   PubMed Web of Science ®Google Scholar
• Cherkasov A , MuratovEN, FourchesDet al. QSAR modeling: where have you been? where are you going to? J. Med. Chem. 57 (12), 4977–5010 (2014).
   PubMed Web of Science ®Google Scholar
• Cumming JG , DavisAM, MuresanS, HaeberleinM, ChenH. Chemical predictive modelling to improve compound quality. Nat. Rev. Drug Disc.12 (12), 948–962 (2013).
   PubMed Web of Science ®Google Scholar
• Rodgers SL , DavisAM, TomkinsonNP, van de WaterbeemdH. Predictivity of simulated ADME AutoQSAR models over time. Mol. Inform.30 (2–3), 256–266 (2011).
   PubMed Web of Science ®Google Scholar
• Cox R , GreenDV, LuscombeCN, MalcolmN, PickettSD. QSAR workbench: automating QSAR modeling to drive compound design. J. Comp. Aided Mol. Des.27 (4), 321–336 (2013).
   PubMed Web of Science ®Google Scholar
• Wood DJ , ButtarD, CummingJG, DavisAM, NorinderU, RodgersSL. Automated QSAR with a hierarchy of global and local models. Mol. Inform.30 (11–12), 960–972 (2011).
   PubMed Web of Science ®Google Scholar
• Stalring JC , CarlssonLA, AlmeidaP, BoyerS. Azorange – high performance open source machine learning for QSAR modeling in a graphical programming environment. J. Cheminform.3, 28 (2011).
   PubMed Web of Science ®Google Scholar
• Tropsha A . Best practices for QSAR model development, validation, and exploitation. Mol. Inform.29 (6–7), 476–488 (2010).
   PubMed Web of Science ®Google Scholar
• Sastry M , LowrieJF, DixonSL, ShermanW. Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J. Chem. Inf. Model.50 (5), 771–784 (2010).
   PubMed Web of Science ®Google Scholar
• Canvas. v2.8 Schrodinger, LLC. NY, USA (2016).
   Google Scholar
• Todeschini R , ConsonniV. Handbook of Molecular Descriptors. MannholdR, KubinyiH, TimmermanH ( Eds). Wiley-VCH, NY, USA (2008).
   Google Scholar
• Hall LH , KierLB. Electrotopological state indices for atom types: a novel combination of electronic, topological, and valence state information. J. Chem. Inf. Comp. Sci.35 (6), 1039–1045 (1995).
   Google Scholar
• Ghose AK , ViswanadhanVN, WendoloskiJJ. Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods: an analysis of Alogp and Clogp methods. J. Phys. Chem. A102 (21), 3762–3772 (1998).
   Web of Science ®Google Scholar
• Ertl P , RohdeB, SelzerP. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem.43 (20), 3714–3717 (2000).
   PubMed Web of Science ®Google Scholar
• Duan J , DixonSL, LowrieJF, ShermanW. Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods. J. Mol. Graph. Model.29 (2), 157–170 (2010).
   PubMed Web of Science ®Google Scholar
• An Y , ShermanW, DixonSL. Hole filling and library optimization: application to commercially available fragment libraries. Bioorg. Med. Chem.20 (18), 5379–5387 (2012).
   PubMed Web of Science ®Google Scholar
• An Y , ShermanW, DixonSL. Kernel-based partial least squares: application to fingerprint-based QSAR with model visualization. J. Chem. Inf. Model.53 (9), 2312–2321 (2013).
   PubMed Web of Science ®Google Scholar
• Bender A , MussaHY, GlenRC, ReilingS. Molecular similarity searching using atom environments, information-based feature selection, and a naive bayesian classifier. J. Chem. Inf. Comp. Sci.44 (1), 170–178 (2004).
   PubMedGoogle Scholar
• Bender A , MussaHY, GlenRC, ReilingS. Similarity searching of chemical databases using atom environment descriptors (MOLPRINT 2D): evaluation of performance. J. Chem. Inf. Comp. Sci.44 (5), 1708–1718 (2004).
   PubMedGoogle Scholar
• Rogers D , BrownRD, HahnM. Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up. J. Biomol. Screen.10 (7), 682–686 (2005).
   PubMedGoogle Scholar
• Dixon S , MerzKMJr, LauriG, IanniJC. QMQSAR: utilization of a semiempirical probe potential in a field-based QSAR method. J. Comp. Chem.26 (1), 23–34 (2005).
   PubMed Web of Science ®Google Scholar
• Dixon SL , SmondyrevAM, KnollEH, RaoSN, ShawDE, FriesnerRA. Phase: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J. Comp. Aided Mol. Des.20 (10–11), 647–671 (2006).
   PubMed Web of Science ®Google Scholar
• Klon AE , LowrieJF, DillerDJ. Improved naive Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction. J. Chem. Inf. Model.46 (5), 1945–1956 (2006).
   PubMed Web of Science ®Google Scholar
• Dixon SL , VillarHO. Investigation of classification methods for the prediction of activity in diverse chemical libraries. J. Comp. Aided Mol. Des.13 (5), 533–545 (1999).
   PubMed Web of Science ®Google Scholar
• Breiman L . Random forests. Machine Learning45 (1), 5–32 (2001).
   Web of Science ®Google Scholar
• Susnow RG , DixonSL. Use of robust classification techniques for the prediction of human cytochrome P450 2d6 Inhibition. J. Chem. Info. Comp. Sci.43 (4), 1308–1315 (2003).
   PubMedGoogle Scholar
• Cheng A , DixonSL. In silico models for the prediction of dose-dependent human hepatotoxicity. J. Comp.Aided Mol. Des.17 (12), 811–823 (2003).
   PubMed Web of Science ®Google Scholar
• Zhang L , ZhuH, OpreaTI, GolbraikhA, TropshaA. QSAR modeling of the blood-brain barrier permeability for diverse organic compounds. Pharm. Res.25 (8), 1902–1914 (2008).
   PubMed Web of Science ®Google Scholar
• Wang J , KrudyG, HouT, ZhangW, HollandG, XuX. Development of reliable aqueous solubility models and their application in druglike analysis. J. Chem. Inf. Model.47 (4), 1395–1404 (2007).
   PubMed Web of Science ®Google Scholar
• Fjodorova N , VrackoM, NovicM, RoncaglioniA, BenfenatiE. New public QSAR model for carcinogenicity. Chem. Cent. J.4 (Suppl. 1), S3 (2010).
   PubMed Web of Science ®Google Scholar
• Ferrari T , GiniG. An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts. Chem. Cent. J.4 (Suppl. 1), S2 (2010).
   PubMed Web of Science ®Google Scholar
• Lombardo A , RoncaglioniA, BorianiE, MilanC, BenfenatiE. Assessment and validation of the caesar predictive model for bioconcentration factor (BCF) in fish. Chem. Cent. J.4 (Suppl. 1), S1 (2010).
   PubMed Web of Science ®Google Scholar
• Dimitrov S , DimitrovaN, ParkertonT, ComberM, BonnellM, MekenyanO. Base-line model for identifying the bioaccumulation potential of chemicals. SAR QSAR Environ. Res.16 (6), 531–554 (2005).
   PubMed Web of Science ®Google Scholar
• Zhao C , BorianiE, ChanaA, RoncaglioniA, BenfenatiE. A new hybrid system of QSAR models for predicting bioconcentration factors (BCF). Chemosphere73 (11), 1701–1707 (2008).
   PubMed Web of Science ®Google Scholar
• Acharjee S , Do-RegoJL, OhDYet al. Molecular cloning, pharmacological characterization, and histochemical distribution of frog vasotocin and mesotocin receptors. J. Mol. Endocrinol.33 (1), 293–313 (2004).
   PubMed Web of Science ®Google Scholar
• Benfenati E , BenigniR, DemariniDMet al. Predictive models for carcinogenicity and mutagenicity: frameworks, state-of-the-art, and perspectives. J. Environ. Sci. Health27 (2), 57–90 (2009).
   Google Scholar
• Piegorsch WW , ZeigerE. Measuring Intra-Assay Agreement for the Ames Salmonella Assay. HothornL ( Ed.). Springer, Berlin, Heidelberg (1991).
   Google Scholar