Identification of selective ligands of CB2 receptor for osteoarthritis tratament utilizing Machine Learning techniques and other computational approaches

• Grant number: 24/14720-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: December 01, 2024
• End date: November 30, 2025
• Field of knowledge: Physical Sciences and Mathematics - Physics - Atomic and Molecular Physics
• Principal Investigator: Kathia Maria Honorio
• Grantee: Francisco Rangel Alves
• Host Institution: Escola de Artes, Ciências e Humanidades (EACH). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

Osteoarthritis (OA) is an inflammatory disease that affects the joints, causing pain and obstructing movement. It is remarkable that one of the sequels of this condition, chronic pain, is fairly difficult to treat, since the medications usually prescribed do not result in a significant improvement of the patient's condition and present dangerous side-effects. Taking into account that the incidents of this affliction have risen severely due to the modern lifestyle, researches focused on developing new and more effective therapies become utterly indispensable. This study has as its main objective discovering compounds capable of interacting selectively with cannabinoid receptor 2 (CB2), directed to osteoarthritis therapy, utilizing modern computational techniques. During this project, many researches will be carried out in various databases to select molecules with known activity for CB2, followed by data curation to select only the viable compounds for the research. The next step is the use of in silico approaches to generate molecular descriptors for the selected compounds, which will serve as a basis to estimate a regression model capable of predicting the selectivity of new molecules against the receptor under study, using machine learning techniques. The expected result is a model capable of filtering active molecules in any data bank, such as ZINC, which may be used as a reference to the production of new and more efficient drugs to treat osteoarthritis.