Mathematical structural descriptors and mutagenicity assessment: a study with congeneric and diverse datasets$.

Quantitative bioactivity and toxicity assessment of chemical compounds plays a central role in drug discovery as it saves a substantial amount of resources. To this end, high-performance computing has enabled researchers and practitioners to leverage hundreds, or even thousands, of computed molecular descriptors for the activity prediction of candidate compounds. In this paper, we evaluate the utility of two large groups of chemical descriptors by such predictive modelling, as well as chemical structure discovery, through empirical analysis. We use a suite of commercially available and in-house software to calculate molecular descriptors for two sets of chemical mutagens - a homogeneous set of 95 amines, and a diverse set of 508 chemicals. Using calculated descriptors, we model the mutagenic activity of these compounds using a number of methods from the statistics and machine-learning literature, and use robust principal component analysis to investigate the low-dimensional subspaces that characterize these chemicals. Our results suggest that combining different sets of descriptors is likely to result in a better predictive model - but that depends on the compounds being modelled and the modelling technique being used.

Carbon multi-shell cages.

Complex chemical compounds found as minerals or synthesized in labs evidenced a multi-shell structure. Also, fullerenes aggregate, randomly or following a well-defined geometry, in multi-shells. The way of space filling differs as a function of the dimensions and shape of the composing small cages. In this paper an attempt to build and evaluate the stability of several fullerene aggregates is made. The results show multi-shell covalently bonded structures with stability comparable to that of C60, the reference fullerene in nanoscience. The calculations were made at the DFTB and DFT levels of theory.

Peptide property modeling by Cluj indices.

The novel Cluj property indices are used for modeling the biological properties of dipeptides: the ACE inhibition activity of a set of 58 dipeptides and the bitter tasting activity of a set of 48 dipeptides, taken from the literature. The results are compared to those reported in some previous works.