New Vertex Indices and their Applications in Evaluating Antileukemic Activity of 9-Anilinoacridines and the Activity of 2′,3′-Dideoxy-Nuclosides Against HIV

Combinatorial Design of Molecule using Activity-Linked Substructural Topological Information as Applied to Antitubercular Compounds

BACKGROUND

Generating a large number of compounds using combinatorial methods increases the possibility of finding novel bioactive compounds. Although some combinatorial structure generation algorithms are available, any method for generating structures from activity-linked substructural topological information is not yet reported.

OBJECTIVE

To develop a method using graph-theoretical techniques for generating structures of antitubercular compounds combinatorially from activity-linked substructural topological information, predict activity and prioritize and screen potential drug candidates.

METHODS

Activity related vertices are identified from datasets composed of both active and inactive or, differently active compounds and structures are generated combinatorially using the topological distance distribution associated with those vertices. Biological activities are predicted using topological distance based vertex indices and a rule based method. Generated structures are prioritized using a newly defined Molecular Priority Score (MPS).

RESULTS

Studies considering a series of Acid Alkyl Ester (AAE) compounds and three known antitubercular drugs show that active compounds can be generated from substructural information of other active compounds for all these classes of compounds. Activity predictions show high level of success rate and a number of highly active AAE compounds produced high MPS score indicating that MPS score may help prioritize and screen potential drug molecules. A possible relation of this work with scaffold hopping and inverse Quantitative Structure-Activity Relationship (iQSAR) problem has also been discussed.

CONCLUSION

The proposed method seems to hold promise for discovering novel therapeutic candidates for combating Tuberculosis and may be useful for discovering novel drug molecules for the treatment of other diseases as well.

Two new atom centered fragment descriptors and scoring function enhance classification of antibacterial activity

Classification of pharmacologic activity of a chemical compound is an essential step in any drug discovery process. We develop two new atom-centered fragment descriptors (vertex indices)--one based solely on topological considerations without discriminating atom or bond types, and another based on topological and electronic features. We also assess their usefulness by devising a method to rank and classify molecules with regard to their antibacterial activity. Classification performances of our method are found to be superior compared to two previous studies on large heterogeneous data sets for hit finding and hit-to-lead studies even though we use much fewer parameters. It is found that for hit finding studies topological features (simple graph) alone provide significant discriminating power, and for hit-to-lead process small but consistent improvement can be made by additionally including electronic features (colored graph). Our approach is simple, interpretable, and suitable for design of molecules as we do not use any physicochemical properties. The singular use of vertex index as descriptor, novel range based feature extraction, and rigorous statistical validation are the key elements of this study.

Topological shape and size of peptides: identification of potential allele specific helper T cell antigenic sites

A database of primary sequences of 28 immunogenic peptides, known to elicit T cell response, derived from five different haplotypes was compiled to identify allele specific helper T cell antigenic sites using a rule based graph-theoretical method. The prediction was based on the identification of allele specific patterns in the form of "topological shape and size" present in the peptides. Indices computed from weighted connected graph models of amino acid side chains and peptides were used in this purpose. The system was trained by 10 Ad and 10 non-Ad restricted peptide sequences, assigned actives and inactives, respectively, chosen randomly from the database, and four Ad and four non-Ad restricted sequences were kept as test peptides. This allowed the system to learn about "topological shape and size" specific for Ad restricted peptides from the differences, if any, they had with the inactive peptides in that respect. The system made 100% correct prediction for the training set peptides and misclassified only one inactive peptide of the test set. The system also identified crucial residues for lambda repressor 12-24 and insulin A-chains. This identification also shows that activity related/crucial residues could be located at varying distances from the peptide terminals. To our knowledge, the method is unique of its kind in the literature and may find application in the rational design of synthetic vaccines and other peptides of immunological importance.

Testing by artificial intelligence: computational alternatives to the determination of mutagenicity

In order to develop methods for evaluating the predictive performance of computer-driven structure-activity methods (SAR) as well as to determine the limits of predictivity, we investigated the behavior of two Salmonella mutagenicity data bases: (a) a subset from the Genetox Program and (b) one from the U.S. National Toxicology Program (NTP). For molecules common to the two data bases, the experimental concordance was 76% when "marginals" were included and 81% when they were excluded. Three SAR methods were evaluated: CASE, MULTICASE and CASE/Graph Indices (CASE/GI). The programs "learned" the Genetox data base and used it to predict NTP molecules that were not present in the Genetox compilation. The concordances were 72, 80 and 47% respectively. Obviously, the MULTICASE version is superior and approaches the 85% interlaboratory variability observed for the Salmonella mutagenicity assays when the latter was carried out under carefully controlled conditions.