Quantitative structure–activity relationship (QSAR) models were developed for the prediction of dermal absorption based on experimental log K _p data for a diverse set of 101 chemicals obtained from the literature. Molecular descriptors including topostructural (TS), topochemical (TC), shape or three-dimensional (3D) and quantum chemical (QC) indices were calculated. Based on this information, a generic predictive model was created using the diverse set of 101 compounds. In addition, two submodels were prepared for subsets of 79 cyclic and 22 acyclic chemicals. A modified Gram–Schmidt variable reduction algorithm for descriptor thinning was followed by regression analyses using ridge regression (RR), principal components regression (PCR) and partial least squares regression (PLS). The RR results were found to be superior to PLS and PCR regressions. The cross-validated correlation coefficients for the full set and subsets were 0.67–0.87. Computational methods such as QSAR modelling can be used to augment existing data to prioritise chemicals that need to be studied further for toxicological evaluation and risk assessment.

The growing incidences of new viral diseases and increasingly frequent viral epidemics have strained therapeutic and preventive measures; the high mutability of viral genes puts additional strains on developmental efforts. Given the high cost and time requirements for new drugs development, vaccines remain as a viable alternative, but there too traditional techniques of live-attenuated or inactivated vaccines have the danger of allergenic reactions and others. Peptide vaccines have, over the last several years, begun to be looked on as more appropriate alternatives, which are economically affordable, require less time for development and hold the promise of multi-valent dosages. The developments in bioinformatics, proteomics, immunogenomics, structural biology and other sciences have spurred the growth of vaccinomics where computer assisted approaches serve to identify suitable peptide targets for eventual development of vaccines. In this mini-review we give a brief overview of some of the recent trends in computer assisted vaccine development with emphasis on the primary selection procedures of probable peptide candidates for vaccine development.