Improved QSAR modeling of anti-HIV-1 acivities by means of the optimized correlation weights of local graph invariants

Abstract

Abstract:We report the results derived from the use of molecular descriptors calculated with the correlation weights (CWs) of local graph invariants for modeling of anti-HIV-1 potencies of two groups of reverse transcriptase inhibitors. The presence of different chemical elements in the molecular structure of the inhibitors and the Morgan extended connectivity values of zeroth-, first-, and second order have been examined as local graph invariants in the labeled hydrogen-filled graphs. We have computed via Monte Carlo optimization procedure the values of CWs which produce the largest possible correlation coefficient between the numerical data on the anti-HIV-1 potencies and those values of the descriptors on the training set. The model of the anti-HIV-1 activity obtained with compounds of training set by means of optimization of correlation weights of chemical elements present together with Morgan extended connectivity of first order makes up a sensible model for a satisfactory prediction of the endpoints of the compounds belonging to the test set.

Models for prediction of anti- HIV-1 activity of 5-Alkyl-2-alkylamino-6-(2,6-difluorophenylalkyl)-3,4-dihydropyrimidin-4(3H)-ones using random forest, decision tree and moving average analysis

“…Since 1991, compounds possessing anti-HIV activity have been the subject of many QSAR studies [22]. The exponential growth of the number of organic compounds and the growing cost of pharmaceutical tests demands the implementation of efficient procedures for prediction of the biological activity [23] possibly in a rapid and cost effective way [24].…”