Logiciels scientifiques > Physicochimie PAGE EN COURS DE TRADUCTION
PrologD is an invaluable tool in the calculation of drug absorption and elimination, and in solving other drug discovery problems. It is also useful when the experimental determination of lipophilicity is difficult or impossible, for example, if logD values which are too high or too low, or if the compound decomposes during the measurement.
CompuDrug´s PrologD calculates a good estimation for the "true" value of logD, which characterizes compounds with dissociable groups, i.e. practically all of the structures, which you will encounter in medicinal chemistry. The calculation accounts for all possible interactions of the dissociable groups by an approach developed by the authors of the software and published (J. Pharm. Sci., 86(7), 865-871, 1997 and J. Pharm. Sci., 86 (10), 1173-1179, 1997). The results are displayed in an easy interpretable graph showing the variation of the logP value. Charges of the ionic strengths can also be accounted for.
Our artificial neural network based approach, using atomic fragmental descriptors, has been developed further to predict the octanol-water partition coefficient (log P) on a wider range of organic compounds. Our present method is based on a trained neural network, but is unique in a way that its input is an extended set of atomic fragmental descriptors.
The developed method has already been published, and the algorithm has been released as part of the version of the Pallas PrologP software.
Our method involves forming the next member of a new class of pseudo-linear algorithms, where the precision of the non-linear approaches is combined with the transparency of earlier linear methods.
The upgrade appears in Pallas for Windows, Pallas for Linux/Unix, Pallas Net, ISIS Plug-in, and Pallas SDK applications.
Since the early 1960's, lipophilicity has proven to be very important molecular description, often well-correlated with the bioactivity of chemical entities. Lipophilicity and hydrophobicity are measured by lipophilic and hydrophobic indices, such as the logarithm of a partition coefficient, which reflects the equilibrium partitioning of a molecule between an non-polar and polar (aqueous) phase.
A new artificial neural network using atomic fragmental descriptors has been developed to predict the octanol-water partition coefficient (logP). The fragmental descriptors were obtained from the Atomic2005 linear logP calculation method implemented in Pallas PrologP program. Using a numerically optimized weighted average of the older methods and the current one, the result is significantly more accurate than the previous method, and provides an exceedingly accurate prediction. The new logP prediction method was implemented into the Pallas 3.4 version.
In order to improve the accuracy of pKa predictions in pKalc software, the majority of acidic and basic groups have been re-trained, using more than 10,000 experimental pKa values collected from the chemical literature.
Thanks to the new inserted parameters, the upgraded version of pKalc provides significantly more accurate predictions.
The upgrade appears in Pallas for Windows, Pallas for Linux/Unix, Pallas Net, ISIS Plug-in, and Pallas SDK applications.
Acid-base properties and protonation-deprotonation equilibria are among the best known chemical phenomena. Observations of the effects of changing the molecular structure on acid-base equilibria have provided much of the theoretical foundation of modern organic chemistry. Many chemists, like analytical chemists, organic chemists, physical chemists, biochemists or molecular biologists often need to know the acidity or basicity of organic compounds, (i.e. the pKa values of the molecules). Sometimes difficulties may arise in the experimental determination of pKa values because it is time-consuming and requires laboratory experience.
In order to improve the accuracy of pKa predictions of the pKalc software, the further acidic and basic groups have been re-parameterized using more than 5,000 experimental aqueous pKa values collected from the chemical literature. The upgrade has significantly increased the accuracy of the prediction, and covers a wider range of the chemical space.
