Abstract Quantitative Structure-Activity Relationship (QSAR) is a computer-aided technology in the field of medicinal chemistry that seeks to clarify the relationships between molecular structures and their biological activities. Such technologies allow for the acceleration of the development of new compounds by reducing the costs of drug design. This work presents 3D-QSARpy, a flexible, user-friendly and robust tool, freely available without registration, to support the generation of QSAR 3D models in an automated way. The user only needs to provide aligned molecular structures and the respective dependent variable. The current version was developed using Python with packages such as scikit-learn and includes various techniques of machine learning for regression. The diverse techniques employed by the tool is a differential compared to known methodologies, such as CoMFA and CoMSIA, because it expands the search space of possible solutions, and in this way increases the chances of obtaining relevant models. Additionally, approaches for select variables (dimension reduction) were implemented in the tool. To evaluate its potentials, experiments were carried out to compare results obtained from the proposed 3D-QSARpy tool with the results from already published works. The results demonstrated that 3D-QSARpy is extremely useful in the field due to its expressive results. StructureActivity Structure Activity (QSAR computeraided computer aided activities design 3DQSARpy, 3DQSARpy DQSARpy QSARpy, QSARpy D flexible userfriendly friendly registration variable scikitlearn scikit learn regression methodologies CoMSIA solutions Additionally dimension reduction potentials works

Abstract We aimed to evaluate the orofacial antinociceptive effect of geraniol in mice and its molecular anchorage mechanism. Seven mice per group (probabilistic sample) were treated with geraniol (12.5, 25 and 50 mg/kg, i.p.), morphine (6 mg/kg, i.p.) and vehicle (saline + Tween 80 at 0.2%, i.p.) 30 minutes prior to the beginning of the experiment. Injecting glutamate (25 μM), capsaicin (2.5 μg) and formalin (2%) into the right upper lip (perinasal) of the mouse induced nociception. Behavioral analysis of the animals considered the friction time (in seconds) of the mentioned region using hind or front paws by a researcher blinded to the treatment groups. The statistical analysis was performed blindly, considering α = 5%. The results showed that in the glutamate and capsaicin tests, concentrations of 25 mg/kg and 50 mg/kg presented antinociceptive activity (p < 0.005, power> 80%). In the formalin test, geraniol was able to reduce nociception at a concentration of 50 mg/kg (p < 0.005, power> 80%). In the molecular anchorage study, high values of binding between the evaluated substance and receptors of glutamate were observed (metabotropic glutamate receptor, -87.8501 Kcal/mol; N-methyl-D-aspartate, -86.4451 Kcal/mol; α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid, -85.6755 Kcal/mol). Geraniol presented orofacial antinociceptive activity, probably by interacting with glutamate-related receptors.

The pharmaceutical industry is increasingly joining chemoinformatics in the search for the development of new drugs to be used in the treatment of diseases. These computational studies have the advantage of being less expensive and optimize the study time, and thus the interest in this area is increasing. Among the techniques used is the development of multitarget directed ligands (MTDLs), which has become an ascending technique, mainly due to the improvement in the quality of treatment involving several drugs. Multitarget therapy is more effective than traditional drug therapy that emphasizes maximum selectivity for a single target. In this review a multitarget drug survey was carried out as a promising strategy in several important diseases: neglected diseases, neurodegenerative diseases, AIDS, and cancer. In addition, we discuss Computer-Aided Drug Design (CADD) techniques as a tool in the projection of multitarget compounds against these diseases.