LogP prediction performance with the SMD solvation model and the M06 density functional family for SAMPL6 blind prediction challenge molecules

Guan, Davy; Lui, Raymond; Matthews, Slade

doi:10.1007/s10822-020-00278-1

Cited by 20 publications

(9 citation statements)

References 23 publications

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Section: Resultsmentioning

confidence: 99%

“…The octanol/water partition coefficient, log P , of compound 22 and 22 -CH 2 was calculated from the solvation free energy differences using the M06-2X functional with SMD implicit solvation and the def2-SVP basis set as described by Guan et al Solvated geometry optimizations were performed using M06-2X/def2-SVP and SMD using water and 1-octanol as solvents. To calculate the log P , the free energies obtained at 298.15 K using octanol and water as solvents were employed to compute the standard free energy associated with the transfer of the solute from the water to octanol phases as …”

Section: Methodsmentioning

confidence: 99%

“…Finally, we assessed the in silico lipophilicity of compounds 22 and 22-CH 2 by computing the log P from the solvation free energy differences using the M06-2X functional with Solvation Model based on Density (SMD) implicit solvation and the def2-SVP basis set. 42 The computed log P of compound 22 is 0.77 while that of 22-CH 2 is 2.44. In line with the above reported experimental solubilities, these results predict a significant decrease in lipophilicity when the oxygen atom is introduced in the adamantane scaffold of compound 22.…”

Section: ■ Introductionmentioning

confidence: 98%

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2-Oxaadamant-1-yl Ureas as Soluble Epoxide Hydrolase Inhibitors: In Vivo Evaluation in a Murine Model of Acute Pancreatitis

Codony¹,

Pujol²,

Pizarro

et al. 2020

J. Med. Chem.

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In vivo pharmacological inhibition of soluble epoxide hydrolase (sEH) reduces inflammation and pain, suggesting sEH as a pharmacological target for the treatment of inflammatory diseases, including acute pancreatitis (AP). Adamantyl ureas are very potent sEH inhibitors but the lipophilicity of the adamantane group compromises their overall solubility. Herein, we report that the replacement of a methylene unit of the adamantane group by an oxygen atom increases the solubility of three series of urea-based sEH inhibitors. Most of these oxa-analogs are nanomolar inhibitors of both the human and murine sEH. Molecular dynamics calculations rationalize the basis for their activity and suggest an explanation for their somehow lower activity vs their hydrocarbon counterparts.Biological profiling of novel 2-oxaadamantane-based sEH inhibitors allowed us to select a candidate for further in vitro and in vivo studies in models of cerulein-induced AP. Both in prophylactic and treatment studies, 2-oxaadamantane 22 diminished the overexpression of inflammatory and endoplasmic reticulum stress markers induced by cerulein and reduced the pancreatic damage.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 98%

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2-Oxaadamant-1-yl Ureas as Soluble Epoxide Hydrolase Inhibitors: In Vivo Evaluation in a Murine Model of Acute Pancreatitis

Codony¹,

Pujol²,

Pizarro

et al. 2020

J. Med. Chem.

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“…496eight methods were fairly diverse. Traditional QM-based physical methods included log P predictions with COSMO-RS method as 497 implemented in COSMOtherm v19 at the BP//TZVPD//FINE Single Point level (hmz0n,[16][17][18]) and the SMD solvation model with 498 the M06 density functional family (dqxk4,[79]). Additionally, two other top QM-based methods seen in this shortlist used EC-RISM 499 theory with wet or dry octanol (j8nwc and qyzjx)[22].…”

mentioning

confidence: 99%

Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II logPChallenge

Işık

Bergazin²,

Fox

et al. 2020

Preprint

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The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the 18 accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to 19 benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar 20 phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the 21 SAMPL6 log P Challenge, we asked participants to make blind predictions of the octanol-water partition coefficients of neutral 22 species of 11 compounds and assessed how well these methods performed absent the complication of protonation state 23 effects. This challenge builds on the SAMPL6 pK a Challenge, which asked participants to predict pK a values of a superset of the 24 compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research 25 groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based 26 empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase 27 in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of octanol-water log P predictions 28 in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral 29 species was necessary for log P and several categories of method benefited from the vast amounts of experimental octanol-water 30 log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included 31 QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A 32 comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average 33 RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92±0.13, 34 0.48±0.06, 0.47±0.05, and 0.50±0.06, respectively. 35 36 0.1 Keywords 37 octanol-water partition coefficient ⋅ log P ⋅ blind prediction challenge ⋅ SAMPL ⋅ free energy calculations ⋅ solvation modeling 38 1 of 50 0.2 Abbreviations 39 SAMPL Statistical Assessment of the Modeling of Proteins and Ligands 40 log P log 10 of the organic solvent-water partition coefficient ( ) of neutral species 41 log D log 10 of organic solvent-water distribution coefficient ( ) 42 pK a −log 10 of the acid dissociation equilibrium constant 43 SEM Standard error of the mean 44 RMSE Root mean squared error 45 MAE Mean absolute error 46 Kendall's rank correlation coefficient (Tau) 47 R 2 Coefficient of determination (R-Squared) 48 QM Quantum Mechanics 49 MM Molecular Mechanics 50 1 Introduction 51 The development of computational biomolecular modeling methodolgoies is motivated by the goal of enabling quantitative 52 molecular design, pred...

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“…Water-octanol partition coefficients were predicted using the JCLogP model in JChem for Excel 19.7.26, 2019, ChemAxon (https://www.chemaxon.com), which was selected as an additional bioavailability descriptor after comparison to other partition coefficient models. 18 A 2.5 uM threshold was applied to the potency values in this dataset to produce a…”

Section: Davy Guan's Modelmentioning

confidence: 99%

An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials

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LogP prediction performance with the SMD solvation model and the M06 density functional family for SAMPL6 blind prediction challenge molecules

Cited by 20 publications

References 23 publications

2-Oxaadamant-1-yl Ureas as Soluble Epoxide Hydrolase Inhibitors: In Vivo Evaluation in a Murine Model of Acute Pancreatitis

2-Oxaadamant-1-yl Ureas as Soluble Epoxide Hydrolase Inhibitors: In Vivo Evaluation in a Murine Model of Acute Pancreatitis

Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II logPChallenge

An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials

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