A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

Abstract

The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R 3 (charge density on substituent at position C 3 ), Q 1 (charge on atom C 1 ), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active, moderately active, and inactive.

Research Article: Insights into the Molecular Requirements for the Anti‐obesity Activity of a Series of CB1 Ligands

“…For this, the generation of molecular fragments for each compound was carried out using the following fragment distinctions: atoms (A), bonds (B), connections (C), hydrogen atoms (H), chirality (Ch), and donor and acceptor (DA). Besides, to assess the hologram generation, several combinations of these parameters were considered using the fragment size default (4–7) as follows: AB, ABC, ABCH, ABCHCh, ABCHChDA, ABH, ABCCh, ABDA, ABCDA, ABHDA, ABCChDA, ABCHDA, and ABHChDA. Another considered option during the HQSAR analyses was the screening of the 12 default series of hologram length values, which ranged from 53 to 401 bins.…”

“…These results also indicate that the inclusion of one fragment distinction (chirality, model 7) did not improve the statistical quality of the model. So, from these results, we decided to use model 2 (A/B/C), with the fragment size default (4–7), in the future analyses.…”

“…It has been investigated for the treatment of a large number of medical illnesses, including glaucoma, hypertension, nausea associated with chemotherapy, pain, migraine and many other disorders (2). Several studies have been performed with the aim to understand possible molecular mechanisms responsible for medicinal and psychoactive properties of cannabinoid compounds (3–7). The pharmacological effects of cannabinoid compounds are mediated by at least two receptors, termed CB 1 and CB 2 , which are two subtypes of G‐protein‐coupled cannabinoid receptors (8).…”

Theoretical models for the antitrypanosomal activity of thiosemicarbazone derivatives

“…Then, the pattern recognition techniques principal component analysis (PCA), hierarchical cluster analysis (HCA), kth‐nearest neighbors (KNN), and soft independent modeling by class analogy (SIMCA) were applied to select the most relevant descriptors and to build classification models for the cruzain inhibition activity exhibited by this series of compounds. These methodologies have been used successfully for several biological targets 15–25. So, with this study, we contributed to understand the molecular requirements for antitrypanosomal activity and consequently in the design of drug candidates to tackle Chagas' disease.…”