Biomedical subjects

A Tropsha

Publications and source records attributed to A Tropsha.

41 records · Page 3Linked to original sources

PubMedDate not supplied

Rational combinatorial library design. 1. Focus-2D: a new approach to the design of targeted combinatorial chemical libraries.

We describe a new computational approach, called Focus-2D, to the rational design of targeted combinatorial chemical libraries. This approach is based on the hypothesis that structurally similar compounds display similar biological activity profiles. Building blocks that are used in a combinatorial chemical synthesis are randomly assembled to produce virtual library compounds. Individual library compounds are represented by Kier-Hall topological descriptors. Molecular similarities between compounds are evaluated quantitatively by modified pairwise Euclidean distances in multidimensional descriptor space. Simulated annealing is used to search the potentially large structural space of virtual chemical libraries to identify compounds similar to lead molecules. Frequency analysis of building block composition of selected virtual compounds identifies building blocks that can be used in combinatorial synthesis of chemical libraries with high similarity to the lead molecules. We show that this method correctly identifies building found in active peptoids with adrenergic or opioid activities.

Drug Design↗

PubMedDate not supplied

Rational combinatorial library design. 3. Simulated annealing guided evaluation (SAGE) of molecular diversity: a novel computational tool for universal library design and database mining.

We have developed a novel method for molecular diversity sampling called SAGE (simulated annealing guided evaluation of molecular diversity). Compounds in chemical databases or virtual combinatorial libraries are conventionally represented as points in multidimensional descriptor space. The SAGE algorithm selects a desired number of optimally diverse points (compounds) from a database. The diversity of a subset of points is measured by a specially designed diversity function, and the most diverse subset is selected using Simulated Annealing (SA) as the optimization tool. Application of SAGE to two simulated data sets of randomly distributed points in two-dimensional space afforded diverse and representative selection as judged by visual inspection. SAGE was also applied, in comparison with random sampling, to two other simulated data sets with points distributed among many clusters. We found that SAGE sampling covered significantly more clusters than the random sampling. By defining a fraction of data points as active, we also compared SAGE with random sampling in terms of hit rates. We showed that when the percentage of active points was low, the hit rates obtained by SAGE were always higher than those obtained by random sampling. When the percentage of active points was high, the performance of SAGE, in terms of individual hit rates, depended upon the data structure. However, in all cases, SAGE performed better than random sampling when cluster hit rates were used as the criterion.

Algorithms↗

PubMedDate not supplied

Automated pharmacophore identification for large chemical data sets.

The identification of three-dimensional pharmacophores from large, heterogeneous data sets is still an unsolved problem. We developed a novel program, SCAMPI (statistical classification of activities of molecules for pharmacophore identification), for this purpose by combining a fast conformation search with recursive partitioning, a data-mining technique, which can easily handle large data sets. The pharmacophore identification process is designed to run recursively, and the conformation spaces are resampled under the constraints of the evolving pharmacophore model. This program is capable of deriving pharmacophores from a data set of 1000-2000 compounds, with thousands of conformations generated for each compound and in less than 1 day of computational time. For two test data sets, the identified pharmacophores are consistent with the known results from the literature.

Angiotensin-Converting Enzyme Inhibitors↗

PubMedDate not supplied

An efficient projection protocol for chemical databases: singular value decomposition combined with truncated-newton minimization.

A rapid algorithm for visualizing large chemical databases in a low-dimensional space (2D or 3D) is presented as a first step in database analysis and design applications. The projection mapping of the compound database (described as vectors in the high-dimensional space of chemical descriptors) is based on the singular value decomposition (SVD) combined with a minimization procedure implemented with the efficient truncated-Newton program package (TNPACK). Numerical experiments on four chemical datasets with real-valued descriptors (ranging from 58 to 27 255 compounds) show that the SVD/TNPACK projection duo achieves a reasonable accuracy in 2D, varying from 30% to about 100% of pairwise distance segments that lie within 10% of the original distances. The lowest percentages, corresponding to scaled datasets, can be made close to 100% with projections onto a 10-dimensional space. We also show that the SVD/TNPACK duo is efficient for minimizing the distance error objective function (especially for scaled datasets), and that TNPACK is much more efficient than a current popular approach of steepest descent minimization in this application context. Applications of our projection technique to similarity and diversity sampling in drug design can be envisioned.

Database Management Systems↗

PubMedDate not supplied

QSAR modeling of datasets with enantioselective compounds using chirality sensitive molecular descriptors.

Shape descriptors used in 3D QSAR studies naturally take into account chirality; however, for flexible and structurally diverse molecules such studies require extensive conformational searching and alignment. QSAR modeling studies of two datasets of fragrance compounds with complex stereochemistry using simple alignment-free chirality sensitive descriptors developed in our laboratories are presented. In the first investigation, 44 alpha-campholenic derivatives with sandalwood odor were represented as derivatives of several common structural templates with substituents numbered according to their relative spatial positions in the molecules. Both molecular and substituent descriptors were used as independent variables in MLR calculations, and the best model was characterized by the training set q2 of 0.79 and external test set r2 of 0.95. In the second study, several types of chirality descriptors were employed in combinatorial QSAR modeling of 98 ambergris fragrance compounds. Among 28 possible combinations of seven types of descriptors and four statistical modeling techniques, k nearest neighbor classification with CoMFA descriptors was initially found to generate the best models with the internal and external accuracies of 76 and 89%, respectively. The same dataset was then studied using novel atom pair chirality descriptors (cAP). The cAP are based on a modified definition of the atomic chirality, in which the seniority of the substituents is defined by their relative partial charge values: higher values correspond to higher seniorities. The resulting models were found to have higher predictive power than those developed with CoMFA descriptors; the best model was characterized by the internal and external accuracies of 82 and 94%, respectively. The success of modeling studies using simple alignment free chirality descriptors discussed in this paper suggests that they should be applied broadly to QSAR studies of many datasets when compound stereochemistry plays an important role in defining their activity.

Ambergris↗