QSAR Modelling of Biological Activity in Cannabinoids with Quantum Similarity Combinations of Charge Fitting Schemes and 3D‐QSAR

Abstract

AbstractQuantitative structure−activity relationship(QSAR) modeled the biological activities of 30 cannabinoids with quantum similarity descriptors(QSD) and Comparative Molecular Field Analysis (CoMFA). The PubChem[https://pubchem.ncbi.nlm.nih.gov/] database provided the geometries, binding affinities(Ki) to the cannabinoid receptors type 1(CB1) and 2(CB2), and the median lethal dose(LD50) to breast cancer cells. An innovative quantum similarity approach combining (self)‐similarity indexes calculated with different charge‐fitting schemes under the Topo‐Geometrical Superposition Algorithm(TGSA) were used to obtain QSARs. The determination coefficient(R2) and leave‐one‐out cross‐validation[Q2(LOO)] quantified the quality of multiple linear regression and support vector machine models. This approach was efficient in predicting the activities, giving predictive and robust models for each endpoint [pLD50: R2=0.9666 and Q2(LOO)=0.9312; pKi(CB1): R2=1.0000 and Q2(LOO)=0.9727, and pKi(CB2): R2=0.9996 and Q2(LOO)=0.9460], where p is the negative logarithm. The descriptors based on the electrostatic potential encrypted better electronic information involved in the interaction. Moreover, the similarity‐based descriptors generated unbiased models independent of an alignment procedure. The obtained models showed better performance than those reported in the literature. An additional 3D‐QSAR CoMFA analysis was applied to 15 cannabinoids, taking THC as a template in a ligand‐based approach. From this analysis, the region surrounding the amino group of the SR141716 ligand is the more favorable for the antitumor activity.