SLIPPER-2001 − Software for Predicting Molecular Properties on the Basis of Physicochemical Descriptors and Structural Similarity

Raevsky, Oleg A.; Trepalin, S. V.; Trepalina, Helen P.; Gerasimenko, Vadim A.; Raevskaja, Olga E.

doi:10.1021/ci010097o

Cited by 57 publications

(37 citation statements)

References 55 publications

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“…Other typical 2D-generated descriptors are related to dispersion forces, polarizability, solute molar volume, and hydrogen bonding acidity and basicity. [44][45][46][47] Descriptors such as log P oct /log D oct , polarizability, polarity, Lewis base and acid strength, and the number and strength of hydrogen bond donors/acceptors obtained from quantum mechanics have also been correlated to permeability. [42,48,49] These descriptors did show high accuracy in the prediction, even though less complex and more rapidly calculated descriptors were almost as accurate.…”

Section: Descriptors Used For Permeability Predictionsmentioning

confidence: 99%

Prediction of ADMET Properties

2006

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This Review describes some of the approaches and techniques used today to derive in silico models for the prediction of ADMET properties. The article also discusses some of the fundamental requirements for deriving statistically sound and predictive ADMET relationships as well as some of the pitfalls and problems encountered during these investigations. It is the intension of the authors to make the reader aware of some of the challenges involved in deriving useful in silico ADMET models for drug development.

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Section: Descriptors Used For Permeability Predictionsmentioning

confidence: 99%

Prediction of ADMET Properties

2006

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“…The average molecular weight and the values of pK a and logP (calculated using the SLIPPER module 17 of the ChemoSoft™ software tool) presented in Table 2 are typical of the databases of organic compounds for bioscreening and are consistent with the widely accepted rules of drug likeness. 18 …”

Section: Databasesmentioning

confidence: 99%

In Silico Estimation of DMSO Solubility of Organic Compounds for Bioscreening

Balakin¹,

Ivanenkov²,

Skorenko³

et al. 2004

SLAS Discovery

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Solubility of organic compounds in DMSO is an important issue for commercial and academic organizations handling large compound collections or performing biological screening. In particular, solubility data are critical for the optimization of storage conditions and for the selection of compounds for bioscreening compatible with the assay protocol. Solubility is largely determined by the solvation energy and the crystal disruption energy, and these molecular phenomena should be assessed in structure-solubility correlation studies. The authors summarize our long-term experimental observations and theoretical studies of physicochemical determinants of DMSO solubility of organic substances. They compiled a comprehensive reference database of proprietary data on compound solubility (55,277 compounds with good DMSO solubility and 10,223 compounds with poor DMSO solubility), calculated specific molecular descriptors (topological, electromagnetic, charge, and lipophilicity parameters), and applied an advanced machine-learning approach for training neural networks to address the solubility. Both supervised (feed-forward, back-propagated neural networks) and unsupervised (Kohonen neural networks) learning methods were used. The resulting neural network models were validated by successfully predicting DMSO solubility of compounds in independent test selections. (Journal of Biomolecular Screening 2004:22-31)

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“…The program package SLIPPER-2001 [25] was used in the final stage of the work. As described above in these calculations Tanimoto indices are estimated for all pairs of compounds of the set and usually three nearest neighbors (NN) are used to predict the property value for a compoundof-interest.…”

Section: Methodsmentioning

confidence: 99%

Analysis of water solubility data on the basis of HYBOT descriptors.

Raevsky

Schaper

2003

QSAR Comb. Sci.

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This work describes the analysis of water-gas phase partitioning data L w C w /C g for 559 organic chemicals on the basis of physicochemical descriptors calculated by the HYBOT program package. Physicochemical descriptors combined with indicator variables as well as a new approach combining traditional QSAR and molecular similarity are used to take structural features into account. The H-bond acceptor ability of chemicals (i.e. interaction of acceptor atoms with hydrogen atoms of water) is the main factor that influences the partitioning of vapors into water. The simultaneous consideration of H-bond acceptor and donor factors leads to a description of the solubility of vapors with a correlation coefficient of about 0.92. The influence of steric interactions of solutes (characterized by means of molecular polarizability) with water molecules contributes slightly but significantly from the statistics point of view. The use of a set of indicator variables for hydrocarbons and for molecules containing amino, amido, CX 3 , ether and nitro groups as well as for molecules with ability to form intramolecular hydrogen bonds improves the correlation and helps to take structural features into account. Furthermore, the application of an approach based on the calculation of additional contributions to solubility by considering −nearest neighbor chemicals× and their difference in physicochemical parameters gives in many cases good results and could be very useful in the analysis of vast data sets.

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SLIPPER-2001 − Software for Predicting Molecular Properties on the Basis of Physicochemical Descriptors and Structural Similarity

Cited by 57 publications

References 55 publications

Prediction of ADMET Properties

Prediction of ADMET Properties

In Silico Estimation of DMSO Solubility of Organic Compounds for Bioscreening

Analysis of water solubility data on the basis of HYBOT descriptors.

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