Predicting the Octanol Solubility of Organic Compounds

Admire, Brittany; Yalkowsky, Samuel H.

doi:10.1002/jps.23561

Cited by 13 publications

(14 citation statements)

References 56 publications

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“…The molar volume of octanol, V o , is equal to 158 (cm 3 /mol) and Admire and Yalkowsky (2013) showed that the octanol solubility parameter, d o , is around 19.5 (J/cm 3 ) 0.5 . V u is calculated using the Fedor's group contribution method (1974).…”

Section: Group Primarymentioning

confidence: 99%

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Estimating the physicochemical properties of polyhalogenated aromatic and aliphatic compounds using UPPER: Part 2. Aqueous solubility, octanol solubility and octanol–water partition coefficient

2015

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Section: Group Primarymentioning

confidence: 99%

“…Similar to aqueous solubility, the solubility of a solute in octanol is related to its CLFR and octanol activity coefficient, c o , by the following equation (2006) and Admire and Yalkowsky (2013) showed that most liquids are miscible with octanol and that Table 1 The entropic and enthalpic descriptors.…”

Section: Octanol Solubility (S O )mentioning

confidence: 99%

Estimating the physicochemical properties of polyhalogenated aromatic and aliphatic compounds using UPPER: Part 2. Aqueous solubility, octanol solubility and octanol–water partition coefficient

2015

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“…Admire and Yakowlsky analyze the use of the general solubility equation (GSE) for predicting the octanol solubility of small molecule compounds 34 shown for the representataive model in Figure 3. The validation dataset consists of only 20 samples and understandably exhibits a very noisy loss profile during training, which makes it difficult to use for early stopping.…”

Section: Octanol Solubilitymentioning

confidence: 99%

“…While a larger internal validation set would alleviate this issue, it would also decrease the amount of data used for actual training. 34 , 81 additional;, b all 245 from ref. 33 Within the Abraham octanol dataset, certain compounds are consistently harder to predict than others.…”

Section: Octanol Solubilitymentioning

confidence: 99%

Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction

Coley

Barzilay

Green

et al. 2017

J. Chem. Inf. Model.

411

362

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The task of learning an expressive molecular representation is central to developing quantitative structure-activity and property relationships. Traditional approaches rely on group additivity rules, empirical measurements or parameters, or generation of thousands of descriptors. In this paper, we employ a convolutional neural network for this embedding task by treating molecules as undirected graphs with attributed nodes and edges. Simple atom and bond attributes are used to construct atom-specific feature vectors that take into account the local chemical environment using different neighborhood radii. By working directly with the full molecular graph, there is a greater opportunity for models to identify important features relevant to a prediction task. Unlike other graph-based approaches, our atom featurization preserves molecule-level spatial information that significantly enhances model performance. Our models learn to identify important features of atom clusters for the prediction of aqueous solubility, octanol solubility, melting point, and toxicity. Extensions and limitations of this strategy are discussed.

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“…The solubility of organic compounds in 1-octanol is important because of its direct relationship to the partition coefficient logP used in pharmacology and environmental chemistry. Current models that can be used to predict 1-octanol solubility include group contribution methods [ 1 ] and often include melting point as a descriptor [ 2 – 4 ]. The most recent model by Admire and Yalkowsky [ 4 ] gives a very useful rule of thumb to predict molar 1-octanol solubility from just the melting point where the compound melting point mp is in °C for compounds that are solid at room temperature and is taken to be 25 for liquids.…”

Section: Introductionmentioning

confidence: 99%

Prediction of 1-octanol solubilities using data from the Open Notebook Science Challenge

Buonaiuto

Lang

2015

Chemistry Central Journal

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Background1-Octanol solubility is important in a variety of applications involving pharmacology and environmental chemistry. Current models are linear in nature and often require foreknowledge of either melting point or aqueous solubility. Here we extend the range of applicability of 1-octanol solubility models by creating a random forest model that can predict 1-octanol solubilities directly from structure.ResultsWe created a random forest model using CDK descriptors that has an out-of-bag (OOB) R2 value of 0.66 and an OOB mean squared error of 0.34. The model has been deployed for general use as a Shiny application.ConclusionThe 1-octanol solubility model provides reasonably accurate predictions of the 1-octanol solubility of organic solutes directly from structure. The model was developed under Open Notebook Science conditions which makes it open, reproducible, and as useful as possible.Graphical abstract:Electronic supplementary materialThe online version of this article (doi:10.1186/s13065-015-0131-2) contains supplementary material, which is available to authorized users.

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Predicting the Octanol Solubility of Organic Compounds

Cited by 13 publications

References 56 publications

Estimating the physicochemical properties of polyhalogenated aromatic and aliphatic compounds using UPPER: Part 2. Aqueous solubility, octanol solubility and octanol–water partition coefficient

Estimating the physicochemical properties of polyhalogenated aromatic and aliphatic compounds using UPPER: Part 2. Aqueous solubility, octanol solubility and octanol–water partition coefficient

Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction

Prediction of 1-octanol solubilities using data from the Open Notebook Science Challenge

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