Molecular fields in quantitative structure–permeation relationships: the VolSurf approach

Cruciani, Gabriele; Crivori, Patrizia; Carrupt, Pierre‐Alain; Testa, Bernard

doi:10.1016/s0166-1280(99)00360-7

Cited by 537 publications

(533 citation statements)

References 23 publications

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“…log BB ) 0.212 + 0.139 log P -0.013HCPSA -0.150n HBA (19) (n ) 78, r ) 0.830, s ) 0.422, F ) 54.6) log BB ) 0.225 + 0.174 log P -0.0138HCPSA -0.000709MW (20) (n ) 78, r ) 0.829, s ) 0.423, F ) 54. 2) or channel, the influence of molecular bulkiness should not be obvious.…”

Section: Resultsmentioning

confidence: 99%

ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors

Hou

2003

J. Chem. Inf. Comput. Sci.

113

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In this paper, QSPR models were developed for in vivo blood-brain partitioning data (logBB) of a large data set consisting of 115 diverse organic compounds. The best model is based on three descriptors: n-octanol/ water partition coefficient calculated using the SLOGP approach, logP; high-charged polar surface areas based on the Gasteiger partial charges, HCPSA, and the excessive molecular weight larger than 360, MW 360 . The model bears good statistical significance, n ) 78, r ) 0.88, q ) 0.86, s ) 0.36, F ) 81.5. The actual prediction potential of the model was validated through two external validation sets of 37 diverse compounds. The predicted results demonstrate that the model bears better prediction potential than many other models and can be used for logBB estimations for drug and drug-like molecules. Comparison of several logP calculation approaches suggests that logP calculated by SLOGP can be used as a significant descriptor for the prediction of molecular transport properties because SLOGP gives the most similar results with CLOGP. The QSPR model indicates that larger polar surface areas have a more negative contribution to logBB, but the absolute partial charges on the atoms surrounded by the polar surfaces should be larger than 0.10|e|. Meanwhile, tight junction membranes limit the size of hydrophilic molecules that can cross the membrane with a molecular weight of approximately 360, because when a molecule's weight is larger than 360 it shows a negative contribution to logBB. The computations of molecular surface, partial charges, logP, and logBB have been accomplished using a program called Drug-BB. Moreover, to improve the efficiency of the computations of logP, we made an extensive reparametrization of SLOGP, and the newly developed SLOGP model is only based on simple atomic addition. Further, we developed a set of parameters to calculate the topological polar surface area (TPSA), thus the high-charged topological polar surface area (HCTPSA) could be estimated from the 2D connection information of a molecule. Adopting the new strategies, the estimations of logP, HCTPSA, and logBB are only based on the topological structure of a molecule and therefore, can be used for fast screening of virtual libraries having millions of molecules.

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Section: Resultsmentioning

confidence: 99%

ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors

Hou

2003

J. Chem. Inf. Comput. Sci.

113

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“…The following seven collections of descriptors have been calculated: CoMFA, 32 CoMMA, 33 MOE, 31 chirality descriptors, 27 MolcoonZ, 35 Dragon, 34 and VolSurf. 36 CoMMA descriptors were used in combination with MOE descriptors. MolconnZ descriptors were used in combination with chirality descriptors.…”

Section: Discussionmentioning

confidence: 99%

Combinatorial QSAR of Ambergris Fragrance Compounds

Kovatcheva

Gelbukh

Oloff

et al. 2004

J. Chem. Inf. Comput. Sci.

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A combinatorial quantitative structure-activity relationships (Combi-QSAR) approach has been developed and applied to a data set of 98 ambergris fragrance compounds with complex stereochemistry. The Combi-QSAR approach explores all possible combinations of different independent descriptor collections and various individual correlation methods to obtain statistically significant models with high internal (for the training set) and external (for the test set) accuracy. Seven different descriptor collections were generated with commercially available MOE, CoMFA, CoMMA, Dragon, VolSurf, and MolconnZ programs; we also included chirality topological descriptors recently developed in our laboratory Tropsha, A. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158). CoMMA descriptors were used in combination with MOE descriptors. MolconnZ descriptors were used in combination with chirality descriptors. Each descriptor collection was combined individually with four correlation methods, including k-nearest neighbors (kNN) classification, Support Vector Machines (SVM), decision trees, and binary QSAR, giving rise to 28 different types of QSAR models. Multiple diverse and representative training and test sets were generated by the divisions of the original data set in two. Each model with high values of leave-one-out cross-validated correct classification rate for the training set was subjected to extensive internal and external validation to avoid overfitting and achieve reliable predictive power. Two validation techniques were employed, i.e., the randomization of the target property (in this case, odor intensity) also known as the Y-randomization test and the assessment of external prediction accuracy using test sets. We demonstrate that not every combination of the data modeling technique and the descriptor collection yields a validated and predictive QSAR model. kNN classification in combination with CoMFA descriptors was found to be the best QSAR approach overall since predictive models with correct classification rates for both training and test sets of 0.7 and higher were obtained for all divisions of the ambergris data set into the training and test sets. Many predictive QSAR models were also found using a combination of kNN classification method with other collections of descriptors. The combinatorial QSAR affords automation, computational efficiency, and higher probability of identifying significant QSAR models for experimental data sets than the traditional approaches that rely on a single QSAR method.

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“…3D structures built with MOE were saved as MOL2 files (the dataset is downloadable in sdf format from https://sites.google.com/site/cassmedchem/ projects/limpet) and submitted to VolSurf+ (version 1.0.7.l, http://www.moldiscovery.com) for the calculation of 82 VolSurf+ descriptors [24]. We used the default settings and four probes: OH2, DRY, N1 and O, that mimic respectively water, hydrophobic, hydrogen bond acceptor and hydrogen bond donor interaction of the compounds with the environment (see Table S2 in the Supplementary Information).…”

Section: Dataset Preparationmentioning

confidence: 99%

Prediction and interpretation of the lipophilicity of small peptides

Visconti

Ermondi

Esposito

2015

J Comput Aided Mol Des

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Peptide-based drug discovery has considerably expanded and solid in silico tools for the prediction of physico-chemical properties of peptides are urgently needed. In this work we tested some combinations of descriptors/algorithms to find the best model to predict log D oct of a series of peptides. To do that we evaluate the models statistical performances but also their skills in providing a reliable deconvolution of the balance of intermolecular forces governing the partitioning phenomenon. Results prove that a PLS model based on VolSurf+ descriptors is the best tool to predict log D oct of neutral and ionised peptides. The mechanistic interpretation also reveals that the inclusion in the chemical structure of a HBD group is more efficient in decreasing lipophilicity than the inclusion of a HBA group.

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Molecular fields in quantitative structure–permeation relationships: the VolSurf approach

Cited by 537 publications

References 23 publications

ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors

ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors

Combinatorial QSAR of Ambergris Fragrance Compounds

Prediction and interpretation of the lipophilicity of small peptides

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