Molecular descriptors of neural networks with chemical significance

The probabilistic neural networks (PNNs) are now being analysed to fix a variety of challenges in the diverse fields of science and technology. In chemical graph theory, there are several tools, such as polynomials, functions, etc. that can be used to characterize different network properties. The neighborhood M-polynomial (NM) is one of those that yields neighborhood degree sum based topological indices in a manner that is less time consuming than the usual approach. In this work, the NM-polynomial of 3-layered and 4-layered probabilistic neural networks are derived. Further, some neighborhood degree sum based topological indices are computed from those polynomials. Applications of the present work are interpreted by investigating the chemical importance of the indices. Some structure property models are derived. The graphical representations of the results are also reported.