Abstract: Artificial intelligence (AI) focuses in producing intelligent modelling, which helps imagining knowledge, cracking problems and decision making. Recently, AI plays an important role various fields of pharmacy like drug discovery, delivery formulation development, polypharmacology, hospital pharmacy, etc. In discovery artificial neural networks (ANNs) deep (DNNs) or recurrent (RNNs) ar...

Sierpinski graphs are a widely observed family of fractal-type relevant to topology, Hanoi Tower mathematics, computer engineering, and around. Chemical implementations graph theory establish significant properties, such as chemical activity, physicochemical thermodynamic pharmacological activities molecular graph. Specific descriptors alluded topological indices helpful predict these propertie...

The acid dissociation (ionization) constant pK(a) is one of the fundamental properties of organic molecules. We have evaluated different computational strategies and models to predict the pK(a) values of substituted phenols using partial atomic charges. Partial atomic charges for 124 phenol molecules were calculated using 83 approaches containing seven theory levels (MP2, HF, B3LYP, BLYP, BP86,...

Novel bond-level molecular descriptors based on linear maps similar to those defined in algebra theory are proposed. The k edge-adjacency matrix (E) denotes the matrix of bond linear indices (non-stochastic) with respect to the canonical basis set. The k stochastic edge-adjacency matrix, ES, is here proposed as a new molecular representation easily calculated from E. Then, the k stochastic bond...

Classical quantitative structure-properties relationship (QSPR) statistical techniques unavoidably present an inherent paradoxical computational context. They rely on the definition of a Gram matrix in descriptor spaces, which is used afterwards to reduce the original dimension via several possible kinds of algebraic manipulations. From there, effective models for the computation of unknown pro...

An overview on the development of QSPR/QSAR equations using various descriptor mining techniques and multilinear regression analysis in the framework of program CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) is given. The description of the methodologies applied in CODESSA is followed by the presentation of the QSAR and QSPR models derived for eighteen molecular act...

This paper presents a density functional theory (DFT)/time-dependent DFT (TD-DFT) study on the lowest lying singlet and triplet excited states of 20 selected polybrominateddiphenyl ether (PBDE) congeners, with the solvation effect included in the calculations using the polarized continuum model (PCM). The results obtained showed that for most of the brominated diphenyl ether (BDE) congeners, th...

Fullerenes are sparingly soluble in many solvents. The dependence of fullerene’s solubility on molecular structure of the solvent must be understood in order to manage efficiently this class of compounds. To find such dependency ab initio quantum-chemical calculations in combination with quantitative structure–property relationship (QSPR) tool were used to model the solubility of fullerene C60 ...

Over the last 20 years, extensive QSAR studies establish an attractive approach to the elucidation of the modern drug chemistry. In the recent years, constant increase in the performance of hardware and software transformed quantitative structure activity relationship (QSAR) and quantitative structure property relationship (QSPR) into powerful and widely used model for the prediction of many bi...