Biomedical subjects

Eric Pellegrini

Publications and source records attributed to Eric Pellegrini.

3 recordsLinked to original sources

PubMed2004

Transition events in one dimension.

The instanton or stationary-phase formula has received much attention recently as a way of determining transition paths in reacting systems. In this paper, we analyze the instanton approach for some one-dimensional problems and compare the results it gives with data from a numerical simulation. We show that a proper comparison of the analytic and numerical results must take into account the boundary conditions used in the numerical simulations and also suggest values for the integration constant in the instanton formula that gives the best agreement with the simulated results.

Journal Article↗

PubMed2003

Development and testing of a de novo drug-design algorithm.

In this article we present an implementation of a de novo drug-design algorithm. The algorithm starts with a molecule placed in the binding site of a protein and then modifies it using a sequential growth approach. This involves successive cycles of suppression of randomly picked groups in the molecule and their replacement by other groups chosen from databanks of linear or cyclic fragments. The algorithm has been coupled with the DYNAMO library which allows the simulation of macromolecules using molecular mechanical and quantum chemical methods. The main body of the article describes the methodologies we use to create, characterize and evaluate putative ligands. We also consider briefly an application of the algorithm to a protein of pharmacological interest, the neuraminidase of the influenza virus, and discuss the strengths and weaknesses of our approach.

Algorithms↗

PubMedDate not supplied

Use of structure descriptors to discriminate between modes of toxic action of phenols.

Two classification models were developed based on a data set of 220 phenols with four associated Modes of Toxic Action (MOA). Counter-propagation neural networks (CPG NN) and multinomial logistic regression (multinom) were used as classification methods. The combination of topological autocorrelation of empirical pi-charge and sigma-electronegativity and of surface autocorrelation of hydrogen-bonding potential resulted in a 21-dimensional model that successfully discriminated between the four MOAs. Its overall predictive power was estimated to 92% using 5-fold cross-validation. Subsequently, a simple score for the distance to the training data was used to determine the prediction space of the model and used in an exploratory study on the phenols contained in the open NCI database. The use of a prediction space metric proved indispensable for the screening of such a diverse database. The prediction space covered by the proposed model is still of rather local nature which is either caused by the limited diversity and size of the training set or by the high dimensionality of the descriptors.

Animals↗