Clustering of atoms relative to vector space in the Z-matrix coordinate system and ‘graphical fingerprint’ analysis of 3D pharmacophore structure

The behavior of a molecule within its environment is governed by chemical fields present in 3D space. However, beyond local descriptors in 3D, the conformations a molecule assumes, and the resulting clusters also play a role in influencing structure–activity models. This study focuses on the clustering of atoms according to the vector space of four atoms aligned in the Z-Matrix Reference system for molecular similarity. Using 3D-QSAR analysis, it was aimed to determine the pharmacophore groups as interaction points in the binding region of the β2-adrenoceptor target of fenoterol stereoisomers. Different types of local reactive descriptors of ligands have been used to elucidate points of interaction with the target. Activity values for ligand-receptor interaction energy were determined using the Levenberg–Marquardt algorithm. Using the Molecular Comparative Electron Topology method, the 3D pharmacophore model (3D-PhaM) was obtained after aligning and superimposing the molecules and was further validated by the molecular docking method. Best guesses were calculated with a non-output validation (LOO-CV) method. Finally, the data were calculated using the ‘graphic fingerprint’ technique. Based on the eLKlopman (Electrostatic LUMO Klopman) descriptor, the Q2 value of this derivative set was calculated as 0.981 and the R 2ext value is calculated as 0.998

Investigation of inhibitory activity of monoamine oxidase A with 4D-QSAR using Fukui indices identifier

© 2020 Elsevier LtdThe inhibitory activity of monoamine oxidase A (MAO-A) was investigated as 4D- Quantitative Structure-Activity Relationship (4D-QSAR) using Molecular Conformer Electron Topological (MCET), a ligand-based method. The activity of MAO-A according to a 3D interaction was explained by a descriptor with the Fukui indices, which represents the hard-soft acid / base reactivity of atoms. The Fukui indices of atoms in clusters was used as the Local Reactive Descriptor (LRD) of the ligand side. Stochastic approaches were made for each sub-cluster proposed by the Genetic Algorithm (GA). The parameters at receptor side of pharmacophore (Pha) were calculated based on the Fukui indices used as descriptors on the ligand side. Pha was determined based on ligand of synthetic derivatives that are MAO-A inhibitors. Molecular conformers with the most suitable atoms with the template conformer can be selected as one of the most suitable spatial structures for interaction with the receptor. This serves to determine the LRD values ​​of the atoms in the conformer Pha. Depending on the values of the descriptors of the ligand at the points of interaction, it may be either an activity-increasing auxiliary groups (AG) or anti-pharmacophore shielding groups (APS). The model was developed with Leave One Out-Cross Validation (LOO-CV), for the 24 molecules in the training set, and was validated for 8 molecules in the test set. Inhibitory activities determined from the established model were compared with experimental results. For the training set and the external cross validation test set compounds, the Q2 (0.829) and R2 (0.818) of the statistical parameters were calculated, respectively