The Experts below are selected from a list of 10608 Experts worldwide ranked by ideXlab platform
Mehtap Isik - One of the best experts on this subject based on the ideXlab platform.
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octanol water Partition Coefficient measurements for the sampl6 blind prediction challenge
Journal of Computer-aided Molecular Design, 2020Co-Authors: Mehtap Isik, Dorothy Levorse, David L Mobley, Timothy Rhodes, John D ChoderaAbstract:Partition Coefficients describe the equilibrium Partitioning of a single, defined charge state of a solute between two liquid phases in contact, typically a neutral solute. Octanol–water Partition Coefficients ($$K_{\rm ow}$$), or their logarithms (log P), are frequently used as a measure of lipophilicity in drug discovery. The Partition Coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based computational models that predict properties of pharmaceutical relevance such as protein-ligand binding affinities or hydration/solvation free energies. The SAMPL6 Part II octanol–water Partition Coefficient prediction challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 $$\hbox {p}{K}_{{\rm a}}$$ prediction challenge in a blind experimental benchmark. Following experimental data collection, the Partition Coefficient dataset was kept blinded until all predictions were collected from participating computational chemistry groups. A total of 91 submissions were received from 27 participating research groups. This paper presents the octanol–water log P dataset for this SAMPL6 Part II Partition Coefficient challenge, which consisted of 11 compounds (six 4-aminoquinazolines, two benzimidazole, one pyrazolo[3,4-d]pyrimidine, one pyridine, one 2-oxoquinoline substructure containing compounds) with log P values in the range of 1.95–4.09. We describe the potentiometric log P measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future log P data collection efforts for the evaluation of computational methods.
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assessing the accuracy of octanol water Partition Coefficient predictions in the sampl6 part ii log p challenge
Journal of Computer-aided Molecular Design, 2020Co-Authors: Mehtap Isik, John D Chodera, Teresa Danielle Bergazin, Thomas R Fox, Andrea Rizzi, David L MobleyAbstract:The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the Octanol-Water Partition Coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 p[Formula: see text] Challenge, which asked participants to predict p[Formula: see text] values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of Octanol-Water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental Octanol-Water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 ± 0.13, 0.48 ± 0.06, 0.47 ± 0.05, and 0.50 ± 0.06, respectively.
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assessing the accuracy of octanol water Partition Coefficient predictions in the sampl6 part ii log p challenge
bioRxiv, 2020Co-Authors: Mehtap Isik, John D Chodera, Teresa Danielle Bergazin, Thomas R Fox, Andrea Rizzi, David L MobleyAbstract:The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the Octanol-Water Partition Coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 pKa Challenge, which asked participants to predict pKa values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of Octanol-Water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental Octanol-Water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92{+/-}0.13, 0.48{+/-}0.06, 0.47{+/-}0.05, and 0.50{+/-}0.06, respectively.
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octanol water Partition Coefficient measurements for the sampl6 blind prediction challenge
bioRxiv, 2019Co-Authors: Mehtap Isik, Dorothy Levorse, David L Mobley, Timothy Rhodes, John D ChoderaAbstract:Partition Coefficients describe the equilibrium Partitioning of a neutral solute between two immiscible phases. Octanol-Water Partition Coefficients (Kow), or their logarithms (log P), are frequently used as a measure of lipophilicity in drug discovery. The Partition Coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based computational models that predict properties of pharmaceutical relevance such as protein-ligand binding affinities or hydration/solvation free energies. The SAMPL6 Part II Octanol-Water Partition Coefficient Prediction Challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 pKa Prediction Challenge in a blind experimental benchmark. Following experimental data collection, the Partition Coefficient dataset was kept blinded until all predictions were collected from participating computational chemistry groups. A total of 91 submissions were received from 27 participating research groups. This paper presents the Octanol-Water log P dataset for this SAMPL6 Part II Partition Coefficient Challenge, which consisted of 11 compounds (six 4-aminoquinazolines, two benzimidazole, one pyrazolo[3,4-d]pyrimidine, one pyridine, one 2-oxoquinoline substructure containing compounds) with log P values in the range of 1.95-4.09. We describe the potentiometric log P measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future log P data collection efforts for the evaluation of computational methods.
John D Chodera - One of the best experts on this subject based on the ideXlab platform.
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octanol water Partition Coefficient measurements for the sampl6 blind prediction challenge
Journal of Computer-aided Molecular Design, 2020Co-Authors: Mehtap Isik, Dorothy Levorse, David L Mobley, Timothy Rhodes, John D ChoderaAbstract:Partition Coefficients describe the equilibrium Partitioning of a single, defined charge state of a solute between two liquid phases in contact, typically a neutral solute. Octanol–water Partition Coefficients ($$K_{\rm ow}$$), or their logarithms (log P), are frequently used as a measure of lipophilicity in drug discovery. The Partition Coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based computational models that predict properties of pharmaceutical relevance such as protein-ligand binding affinities or hydration/solvation free energies. The SAMPL6 Part II octanol–water Partition Coefficient prediction challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 $$\hbox {p}{K}_{{\rm a}}$$ prediction challenge in a blind experimental benchmark. Following experimental data collection, the Partition Coefficient dataset was kept blinded until all predictions were collected from participating computational chemistry groups. A total of 91 submissions were received from 27 participating research groups. This paper presents the octanol–water log P dataset for this SAMPL6 Part II Partition Coefficient challenge, which consisted of 11 compounds (six 4-aminoquinazolines, two benzimidazole, one pyrazolo[3,4-d]pyrimidine, one pyridine, one 2-oxoquinoline substructure containing compounds) with log P values in the range of 1.95–4.09. We describe the potentiometric log P measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future log P data collection efforts for the evaluation of computational methods.
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assessing the accuracy of octanol water Partition Coefficient predictions in the sampl6 part ii log p challenge
Journal of Computer-aided Molecular Design, 2020Co-Authors: Mehtap Isik, John D Chodera, Teresa Danielle Bergazin, Thomas R Fox, Andrea Rizzi, David L MobleyAbstract:The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the Octanol-Water Partition Coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 p[Formula: see text] Challenge, which asked participants to predict p[Formula: see text] values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of Octanol-Water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental Octanol-Water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 ± 0.13, 0.48 ± 0.06, 0.47 ± 0.05, and 0.50 ± 0.06, respectively.
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assessing the accuracy of octanol water Partition Coefficient predictions in the sampl6 part ii log p challenge
bioRxiv, 2020Co-Authors: Mehtap Isik, John D Chodera, Teresa Danielle Bergazin, Thomas R Fox, Andrea Rizzi, David L MobleyAbstract:The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the Octanol-Water Partition Coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 pKa Challenge, which asked participants to predict pKa values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of Octanol-Water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental Octanol-Water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92{+/-}0.13, 0.48{+/-}0.06, 0.47{+/-}0.05, and 0.50{+/-}0.06, respectively.
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octanol water Partition Coefficient measurements for the sampl6 blind prediction challenge
bioRxiv, 2019Co-Authors: Mehtap Isik, Dorothy Levorse, David L Mobley, Timothy Rhodes, John D ChoderaAbstract:Partition Coefficients describe the equilibrium Partitioning of a neutral solute between two immiscible phases. Octanol-Water Partition Coefficients (Kow), or their logarithms (log P), are frequently used as a measure of lipophilicity in drug discovery. The Partition Coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based computational models that predict properties of pharmaceutical relevance such as protein-ligand binding affinities or hydration/solvation free energies. The SAMPL6 Part II Octanol-Water Partition Coefficient Prediction Challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 pKa Prediction Challenge in a blind experimental benchmark. Following experimental data collection, the Partition Coefficient dataset was kept blinded until all predictions were collected from participating computational chemistry groups. A total of 91 submissions were received from 27 participating research groups. This paper presents the Octanol-Water log P dataset for this SAMPL6 Part II Partition Coefficient Challenge, which consisted of 11 compounds (six 4-aminoquinazolines, two benzimidazole, one pyrazolo[3,4-d]pyrimidine, one pyridine, one 2-oxoquinoline substructure containing compounds) with log P values in the range of 1.95-4.09. We describe the potentiometric log P measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future log P data collection efforts for the evaluation of computational methods.
David L Mobley - One of the best experts on this subject based on the ideXlab platform.
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octanol water Partition Coefficient measurements for the sampl6 blind prediction challenge
Journal of Computer-aided Molecular Design, 2020Co-Authors: Mehtap Isik, Dorothy Levorse, David L Mobley, Timothy Rhodes, John D ChoderaAbstract:Partition Coefficients describe the equilibrium Partitioning of a single, defined charge state of a solute between two liquid phases in contact, typically a neutral solute. Octanol–water Partition Coefficients ($$K_{\rm ow}$$), or their logarithms (log P), are frequently used as a measure of lipophilicity in drug discovery. The Partition Coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based computational models that predict properties of pharmaceutical relevance such as protein-ligand binding affinities or hydration/solvation free energies. The SAMPL6 Part II octanol–water Partition Coefficient prediction challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 $$\hbox {p}{K}_{{\rm a}}$$ prediction challenge in a blind experimental benchmark. Following experimental data collection, the Partition Coefficient dataset was kept blinded until all predictions were collected from participating computational chemistry groups. A total of 91 submissions were received from 27 participating research groups. This paper presents the octanol–water log P dataset for this SAMPL6 Part II Partition Coefficient challenge, which consisted of 11 compounds (six 4-aminoquinazolines, two benzimidazole, one pyrazolo[3,4-d]pyrimidine, one pyridine, one 2-oxoquinoline substructure containing compounds) with log P values in the range of 1.95–4.09. We describe the potentiometric log P measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future log P data collection efforts for the evaluation of computational methods.
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assessing the accuracy of octanol water Partition Coefficient predictions in the sampl6 part ii log p challenge
Journal of Computer-aided Molecular Design, 2020Co-Authors: Mehtap Isik, John D Chodera, Teresa Danielle Bergazin, Thomas R Fox, Andrea Rizzi, David L MobleyAbstract:The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the Octanol-Water Partition Coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 p[Formula: see text] Challenge, which asked participants to predict p[Formula: see text] values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of Octanol-Water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental Octanol-Water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 ± 0.13, 0.48 ± 0.06, 0.47 ± 0.05, and 0.50 ± 0.06, respectively.
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assessing the accuracy of octanol water Partition Coefficient predictions in the sampl6 part ii log p challenge
bioRxiv, 2020Co-Authors: Mehtap Isik, John D Chodera, Teresa Danielle Bergazin, Thomas R Fox, Andrea Rizzi, David L MobleyAbstract:The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the Octanol-Water Partition Coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 pKa Challenge, which asked participants to predict pKa values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of Octanol-Water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental Octanol-Water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92{+/-}0.13, 0.48{+/-}0.06, 0.47{+/-}0.05, and 0.50{+/-}0.06, respectively.
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octanol water Partition Coefficient measurements for the sampl6 blind prediction challenge
bioRxiv, 2019Co-Authors: Mehtap Isik, Dorothy Levorse, David L Mobley, Timothy Rhodes, John D ChoderaAbstract:Partition Coefficients describe the equilibrium Partitioning of a neutral solute between two immiscible phases. Octanol-Water Partition Coefficients (Kow), or their logarithms (log P), are frequently used as a measure of lipophilicity in drug discovery. The Partition Coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based computational models that predict properties of pharmaceutical relevance such as protein-ligand binding affinities or hydration/solvation free energies. The SAMPL6 Part II Octanol-Water Partition Coefficient Prediction Challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 pKa Prediction Challenge in a blind experimental benchmark. Following experimental data collection, the Partition Coefficient dataset was kept blinded until all predictions were collected from participating computational chemistry groups. A total of 91 submissions were received from 27 participating research groups. This paper presents the Octanol-Water log P dataset for this SAMPL6 Part II Partition Coefficient Challenge, which consisted of 11 compounds (six 4-aminoquinazolines, two benzimidazole, one pyrazolo[3,4-d]pyrimidine, one pyridine, one 2-oxoquinoline substructure containing compounds) with log P values in the range of 1.95-4.09. We describe the potentiometric log P measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future log P data collection efforts for the evaluation of computational methods.
Julie L Markham - One of the best experts on this subject based on the ideXlab platform.
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determination of octanol water Partition Coefficient for terpenoids using reversed phase high performance liquid chromatography
Journal of Chromatography A, 1999Co-Authors: Shane G Griffin, Grant S Wyllie, Julie L MarkhamAbstract:Octanol–water Partition Coefficients (Kow) for 57 terpenoids were measured using a RP-HPLC method. Sample detection was achieved with standard UV and refractive index detectors and required no special column treatment. Measured log Kow values for the terpenoids ranged from 1.81 to 4.48 with a standard error of between 0.03 and 0.08 over the entire range. Partition Coefficients determined by the RP-HPLC method were compared against shake flask, atom/fragment contribution, fragment and atomistic methods. The HPLC values were found to give the best correlation with shake flask results. Log Kow values calculated by the atom/fragment contribution method gave the best correlation with the HPLC values when compared to fragment and atomistic methods.
Stanley I Sandler - One of the best experts on this subject based on the ideXlab platform.
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a predictive model for the solubility and octanol water Partition Coefficient of pharmaceuticals
Journal of Chemical & Engineering Data, 2011Co-Authors: Chiehming Hsieh, Shu Wang, Shiangtai Lin, Stanley I SandlerAbstract:The prediction of drug solubility in various pure and mixed solvents and the octanol−water Partition Coefficient (KOW) are evaluated using the recently revised conductor-like screening segment activity Coefficient (COSMO-SAC) model. The solubility data of 51 drug compounds in 37 different solvents and their combinations over a temperature range of 273.15 K to 323.15 K (300 systems, 2918 data points) are calculated from the COSMO-SAC model and compared to experiments. The solubility data cover a wide range of solubility from (10−1 to 10−6) in mole fraction. When only the heat of fusion and the normal melting temperature of the drug are used, the average absolute error from the revised model is found to be 236 %, a significant reduction from that (388 %) of the original COSMO-SAC model. When the pure drug properties (heat of fusion and melting temperature) are not available, predictions can still be made with a similar accuracy using the solubility data of the drug in any other solvent or solvent mixture. T...
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determination of infinite dilution activity Coefficients and 1 octanol water Partition Coefficients of volatile organic pollutants
Journal of Chemical & Engineering Data, 1994Co-Authors: Ginger Tse, Stanley I SandlerAbstract:The characterization of pollutants is of growing interest as concerns about the environment increase. One parameter useful in predicting the fate of a chemical in the environment, the infinite dilution activity Coefficient, has been determined here for several EPA priority pollutants in 1-octanol at 25 C using a relative gas-liquid chromatographic measurement technique. A simple correlation has been developed relating the limiting activity Coefficients of a species in pure water and in pure 1-octanol to its octanol/water Partition Coefficient. Agreement between the experimental results and published values is very good. The method developed here of computing the octanol/water Partition Coefficient from gas chromatographic measurements of its infinite dilution activity Coefficients is an improvement over traditional Partition Coefficient determination methods in that it is easier and quicker, without a loss of accuracy. Furthermore, the authors show that this method is applicable to chemicals covering a large range of hydrophobicities (1.0 < log K[sub OW] < 5.0).
