Comparative assessment of physics-based in silico methods to calculate relative solubilities

Zhou SF, Zhong WZ (2017) Drug design and discovery: principles and applications. Molecules 22:279

Article  PubMed  PubMed Central  Google Scholar 

Chung TDY, Terry DB, Smith LH (2004) In vitro and in vivo assessment of ADME and PK properties during lead selection and lead optimization—guidelines, benchmarks and rules of thumb. In: Markossian S, Grossman A, Brimacombe K, Arkin M, Auld D, Austin C, Baell J, Chung TDY, Coussens NP, Dahlin JL, Devanarayan V, Foley TL, Glicksman M, Haas JV, Hall MD, Hoare S, Inglese J, Iversen PW, Kales SC, Lal-Nag M, Li Z, McGee J, McManus O, Riss T, Saradjian P, Sittampalam GS, Tarselli M, Trask OJ Jr, Wang Y, Weidner JR, Wildey MJ, Wilson K, Xia M, Xu X (eds) Assay guidance manual. Bethesda, MD

Google Scholar 

Göller AH, Kuhnke L, Montanari F, Bonin A, Schneckener S, ter Laak A, Wichard J, Lobell M, Hillisch A (2020) Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades. Drug Discov Today 25:1702–1709

Article  PubMed  Google Scholar 

Göller AH, Kuhnke L, ter Laak A, Meier K, Hillisch A (2022) Machine learning applied to the modeling of pharmacological and ADMETAbsorption, distribution, metabolism, excretion and toxicity (ADMET) endpoints. In: Heifetz A (ed) Artificial intelligence in drug design. Springer, New York, pp 61–101

Chapter  Google Scholar 

Lucas AJ, Sproston JL, Barton P, Riley RJ (2019) Estimating human ADME properties, pharmacokinetic parameters and likely clinical dose in drug discovery. Expert Opin Drug Discov 14:1313–1327

Article  CAS  PubMed  Google Scholar 

Eleftheriadou D, Luette S, Kneuer C (2019) In silico prediction of dermal absorption of pesticides—an evaluation of selected models against results from in vitro testing. SAR QSAR Environ Res 30:561–585

Article  CAS  PubMed  Google Scholar 

Elliott JR, Compton RG (2022) Modeling transcuticular uptake from particle-based formulations of lipophilic products. ACS Agric Sci Technol 2:603–614

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hayet, M., Fernandez, V. (2012) Estimation of the solubility parameters of model plant surfaces and agrochemicals: a valuable tool for understanding plant surface interactions. Theor Biol Med Model 9:45

Article  Google Scholar 

Xiao S, Gong Y, Li Z, Fantke P (2021) Improving pesticide uptake modeling into potatoes: considering tuber growth dynamics. J Agric Food Chem 69:3607–3616

Article  CAS  PubMed  Google Scholar 

Avdeef A, Fuguet E, Llinàs A, Ràfols C, Bosch E, Völgyi G, Verbić T, Boldyreva E, Takács-Novák K (2016) Equilibrium solubility measurement of ionizable drugs–consensus recommendations for improving data quality. ADMET DMPK 4:117–178

Article  Google Scholar 

Fink C, Sun DJ, Wagner K, Schneider M, Bauer H, Dolgos H, Mader K, Peters SA (2020) Evaluating the role of solubility in oral absorption of poorly water-soluble drugs using physiologically-based pharmacokinetic modeling. Clin Pharmacol Ther 107:650–661

Article  CAS  PubMed  Google Scholar 

Llinas A, Avdeef A (2019) Solubility challenge revisited after ten years, with multilab shake-flask data, using tight (SD ∼ 0.17 log) and Loose (SD ∼ 0.62 log) Test Sets. J Chem Inf Model 59:3036–3040

Article  CAS  PubMed  Google Scholar 

Ono A, Matsumura N, Kimoto T, Akiyama Y, Funaki S, Tamura N, Hayashi S, Kojima Y, Fushimi M, Sudaki H, Aihara R, Haruna Y, Jiko M, Iwasaki M, Fujita T, Sugano K (2019) Harmonizing solubility measurement to lower inter-laboratory variance—progress of consortium of biopharmaceutical tools (CoBiTo) in Japan. ADMET DMPK 7:183–195

Article  PubMed  PubMed Central  Google Scholar 

Bergstroem CAS, Luthman K, Artursson P (2004) Accuracy of calculated pH-dependent Aqueous Drug Solubility. Eur J Pharm Sci 22:387–398

Article  Google Scholar 

Loh ZH, Samanta AK, Heng PWS (2015) Overview of milling techniques for improving the solubility of poorly water-soluble drugs. Assian J Pharm Sci 10:255–274. https://doi.org/10.1016/j.ajps.2014.12.006

Article  Google Scholar 

Fredenslund Aa (1989) UNIFAC and related group-contribution models for phase equilibria. Fluid Phase Equilib 52:135–150

Article  CAS  Google Scholar 

Bustamante P, Escalera B, Martin A, Selles E (1993) A modification of the extended Hildebrand approach to predict the solubility of structurally related drugs in solvent mixtures. J Pharm Pharmacol 45:253–257

Article  CAS  PubMed  Google Scholar 

Lin HM, Nash RA (1993) An experimental method for determining the Hildebrand solubility parameter of organic nonelectrolytes. J Pharm Sci 82:1018–1026

Article  CAS  PubMed  Google Scholar 

Hansen CM (2007) Hansen solubility parameters: a user’s handbook. CRC Press

Book  Google Scholar 

Delaney JS (2005) Predicting aqueous solubility from structure. Drug Discov Today 10:289–295

Article  CAS  PubMed  Google Scholar 

Balakin KV, Savchuk NP, Tetko IV (2006) In silico approaches to prediction of aqueous and DMSO solubility of drug-like compounds: trends, problems and solutions. Curr Med Chem 13:223–241

Article  CAS  PubMed  Google Scholar 

Faller B, Ertl P (2007) Computational approaches to determine drug solubility Adv. Drug Delivery Rev 59:533–545

Article  CAS  Google Scholar 

Göller AH, Hennemann M, Keldenich J, Clark T (2006) In silico prediction of buffer solubility based on quantum-mechanical and HQSAR- and topology-based descriptors. J Chem Inf Model 46:648–658. https://doi.org/10.1021/ci0503210

Article  CAS  PubMed  Google Scholar 

Schwaighofer A, Schroeter T, Mika S, Laub J, ter Laak A, Sülzle D, Ganzer U, Heinrich N, MÃ (2007) Accurate solubility prediction with error bars for electrolytes: a machine learning approach. J Chem Inf Model 47:407–424. https://doi.org/10.1021/ci600205g

Article  CAS  PubMed  Google Scholar 

Schroeter T, Schwaighofer A, Mika S, ter Laak A, Sülzle D, Ganzer U, Heinrich N, Müller K-R (2007) Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules. J Comput Aided Mol Des 21:651–664. https://doi.org/10.1007/s10822-007-9160-9

Article  CAS  PubMed  Google Scholar 

Montanari F, Kuhnke L, ter Laak A, Clever D-A (2020) Modeling physico-chemical ADMET endpoints with multitask graph convolutional networks. Molecules 25:44–56. https://doi.org/10.3390/molecules25010044

Article  CAS  Google Scholar 

Bonin A, Montanari F, Niederführ S et al (2023) pH-dependent solubility prediction for optimized drug absorption and compound uptake by plants. J Comput Aided Mol Des 37:129–145

Article  CAS  PubMed  Google Scholar 

Gheta SKO, Bonin A, Gerlach T, Göller AH (2023) Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state. J Comput Aided Mol Des 37:765–789. https://doi.org/10.1007/s10822-023-00538-w

Article  CAS  PubMed  Google Scholar 

Klingspohn W, Mathea M, Ter Laak A, Heinrich N, Baumann K (2017) Efficiency of different measures for defining the applicability domain of classification models. J Chem 9(1):44

Google Scholar 

Khanna V, Anwar J, Frenkel D, Doherty MF, Peters B (2021) Free energies of crystals computed using Einstein crystal with fixed center of mass and differing spring constants. J Chem Phys 154(164509):164509. https://doi.org/10.1063/5.0044833

Article  CAS  PubMed  Google Scholar 

Palmer DS, McDonagh JL, Mitchell JBO, van Mourik T, Fedorov MV (2012) First-principles calculation of the intrinsic aqueous solubility of crystalline druglike molecules. J Chem Theory Comput 8:3322–3337

Article  CAS  PubMed  Google Scholar 

Aguilar B, Onufriev AV (2012) Efficient computation of the total solvation energy of small molecules via the r6 generalized born model. J Chem Theory Comput 8:2404–2411

Article  CAS  PubMed  Google Scholar 

Chebil L, Chipot C, Archambault F, Humeau C, Engasser JM, Ghoul M, Dehez F (2010) Solubilities Inferred from the combination of experiment and simulation. Case study of quercetin in a variety of solvents. J Phys Chem B 114:12308–12313

Article  CAS  PubMed 

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