Gogishvili D, Nittinger E, Margreitter C, Tyrchan C (2021) Nonadditivity in public and inhouse data: implications for drug design. J Cheminformatics 13:47. https://doi.org/10.1186/s13321-021-00525-z
Biela A, Betz M, Heine A, Klebe G (2012) Water makes the difference: rearrangement of water solvation layer triggers non-additivity of functional group contributions in protein-ligand binding. ChemMedChem 7:1423–1434. https://doi.org/10.1002/cmdc.201200206
Article CAS PubMed Google Scholar
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
Article CAS PubMed PubMed Central Google Scholar
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. https://doi.org/10.1021/acs.jmedchem.8b00713
Article CAS PubMed Google Scholar
Kramer C (2019) Nonadditivity Analysis. J Chem Inf Model 59:4034–4042. https://doi.org/10.1021/acs.jcim.9b00631
Article CAS PubMed Google Scholar
Krummenacher D, He W, Kuhn B et al (2023) Discovery of orally available and Brain Penetrant AEP inhibitors. J Med Chem 66:17026–17043. https://doi.org/10.1021/acs.jmedchem.3c01804
Article CAS PubMed Google Scholar
Hunziker D, Reinehr S, Palmhof M et al (2022) Synthesis, characterization, and in vivo evaluation of a novel potent autotaxin-inhibitor. Front Pharmacol 12
Hilpert H, Guba W, Woltering TJ et al (2013) β-Secretase (BACE1) inhibitors with high in vivo efficacy suitable for clinical evaluation in Alzheimer’s Disease. J Med Chem 56:3980–3995. https://doi.org/10.1021/jm400225m
Article CAS PubMed Google Scholar
Nettekoven M, Adam J-M, Bendels S et al (2016) Novel triazolopyrimidine-derived cannabinoid receptor 2 agonists as potential treatment for inflammatory kidney diseases. ChemMedChem 11:179–189. https://doi.org/10.1002/cmdc.201500218
Article CAS PubMed Google Scholar
Richter H, Satz AL, Bedoucha M et al (2019) DNA-Encoded Library-Derived DDR1 inhibitor prevents fibrosis and renal function loss in a genetic mouse model of Alport Syndrome. ACS Chem Biol 14:37–49. https://doi.org/10.1021/acschembio.8b00866
Article CAS PubMed Google Scholar
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. https://doi.org/10.1016/j.bmcl.2007.03.072
Article CAS PubMed Google Scholar
Pinard E, Alanine A, Alberati D et al (2010) Selective GlyT1 inhibitors: Discovery of [4-(3-Fluoro-5-trifluoromethylpyridin-2-yl)piperazin-1-yl][5-methanesulfonyl-2-((S)-2,2,2-trifluoro-1-methylethoxy)phenyl]methanone (RG1678), a Promising Novel Medicine to treat Schizophrenia. J Med Chem 53:4603–4614. https://doi.org/10.1021/jm100210p
Article CAS PubMed Google Scholar
Tosstorff A, Rudolph MG, Cole JC et al (2022) A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios. J Comput Aided Mol Des 36:753–765. https://doi.org/10.1007/s10822-022-00478-x
Article CAS PubMed Google Scholar
Ratni H, Karp GM, Weetall M et al (2016) Specific Correction of Alternative Survival Motor Neuron 2 splicing by small molecules: Discovery of a potential Novel Medicine to treat spinal muscular atrophy. J Med Chem 59:6086–6100. https://doi.org/10.1021/acs.jmedchem.6b00459
Article CAS PubMed Google Scholar
Alsenz J, Kansy M (2007) High throughput solubility measurement in drug discovery and development. Adv Drug Deliv Rev 59:546–567. https://doi.org/10.1016/j.addr.2007.05.007
Article CAS PubMed Google Scholar
Wagner B, Fischer H, Kansy M et al (2015) Carrier mediated distribution system (CAMDIS): a new approach for the measurement of octanol/water distribution coefficients. Eur J Pharm Sci 68:68–77. https://doi.org/10.1016/j.ejps.2014.12.009
Article CAS PubMed Google Scholar
Chen X, Murawski A, Patel K et al (2008) A Novel Design of Artificial membrane for improving the PAMPA Model. Pharm Res 25:1511–1520. https://doi.org/10.1007/s11095-007-9517-8
Article CAS PubMed Google Scholar
Wildman SA, Crippen GM (1999) Prediction of Physicochemical parameters by Atomic contributions. J Chem Inf Comput Sci 39:868–873. https://doi.org/10.1021/ci990307l
Kramer C, Dahl G, Tyrchan C, Ulander J (2016) A comprehensive company database analysis of biological assay variability. Drug Discov Today 21:1213–1221. https://doi.org/10.1016/j.drudis.2016.03.015
Pedregosa F, Varoquaux G, Gramfort A et al Scikit-learn: machine learning in Python. Mach Learn PYTHON
Xiong Z, Wang D, Liu X et al (2020) Pushing the boundaries of molecular representation for Drug Discovery with the graph attention mechanism. J Med Chem 63:8749–8760. https://doi.org/10.1021/acs.jmedchem.9b00959
Article CAS PubMed Google Scholar
Paszke A, Gross S, Massa F et al (2019) PyTorch: an imperative style, High-Performance Deep Learning Library. Advances in neural information Processing systems. Curran Associates, Inc
RDKit Open-source cheminformatics
Dalke A, Hert J, Kramer C (2018) J Chem Inf Model 58:902–910. https://doi.org/10.1021/acs.jcim.8b00173. mmpdb: An Open-Source Matched Molecular Pair Platform for Large Multiproperty Data Sets
Leach AG, Pilling EA, Rabow AA et al (2012) Enantiomeric pairs reveal that key medicinal chemistry parameters vary more than simple physical property based models can explain. MedChemComm 3:528–540. https://doi.org/10.1039/C2MD20010D
Hall LH, Kier LB (1991) The Molecular Connectivity Chi indexes and Kappa shape indexes in Structure-Property Modeling. Reviews in Computational Chemistry. Wiley, Ltd, pp 367–422
Kwapien K, Nittinger E, He J et al (2022) Implications of Additivity and Nonadditivity for Machine Learning and Deep Learning models in Drug Design. ACS Omega 7:26573–26581. https://doi.org/10.1021/acsomega.2c02738
Article CAS PubMed PubMed Central Google Scholar
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
Article CAS PubMed Google Scholar
Veber DF, Johnson SR, Cheng H-Y et al (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623. https://doi.org/10.1021/jm020017n
Article CAS PubMed Google Scholar
Diukendjieva A, Tsakovska I, Alov P et al (2019) Advances in the prediction of gastrointestinal absorption: quantitative structure-activity relationship (QSAR) modelling of PAMPA permeability. Comput Toxicol 10:51–59. https://doi.org/10.1016/j.comtox.2018.12.008
Dossetter AG (2012) A matched molecular pair analysis of in vitro human microsomal metabolic stability measurements for methylene substitution or replacements – identification of those transforms more likely to have beneficial effects. MedChemComm 3:1518. https://doi.org/10.1039/c2md20226c
van Tilborg D, Alenicheva A, Grisoni F (2022) Exposing the Limitations of Molecular Machine Learning with Activity cliffs. J Chem Inf Model. https://doi.org/10.1021/acs.jcim.2c01073
Article PubMed PubMed Central Google Scholar
Tamura S, Miyao T, Bajorath J (2023) Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity. J Cheminformatics 15:4. https://doi.org/10.1186/s13321-022-00676-7
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