Combining machine learning, molecular dynamics, and free energy analysis for (5HT)-2A receptor modulator classification.

The 5-Hydroxytryptamine (5HT)-2A receptor, a key target in psychoactive drug development, presents significant challenges in the design of selective compounds. Here, we describe the construction, evaluation and validation of two machine learning (ML) models for the classification of bioactivity mechanisms against the (5HT)-2A receptor. Employing neural networks and XGBoost models, we achieved an overall accuracy of around 87 %, which was further enhanced through molecular modelling (MM) (e.

Targeting cytotoxic lymphocyte antigen 4 (CTLA-4) in breast cancer.

Breast cancer (BC) has a high mortality rate and is one of the most common malignancies in the world. Initially, BC was considered non-immunogenic, but a paradigm shift occurred with the discovery of tumor-infiltrating lymphocytes (TILs) and regulatory T cells (Tregs) in the BC tumor microenvironment. CTLA-4 (Cytotoxic T-lymphocyte-associated protein 4) immunotherapy has emerged as a treatment option for BC, but it has limitations, including suboptimal antitumor effects and toxicity.

Discovery of allosteric SHP2 inhibitors through ensemble-based consensus molecular docking, endpoint and absolute binding free energy calculations.

SHP2 (Src homology-2 domain-containing protein tyrosine phosphatase-2) is a cytoplasmic protein -tyrosine phosphatase encoded by the gene PTPN11. It plays a crucial role in regulating cell growth and differentiation. Specifically, SHP2 is an oncoprotein associated with developmental pathologies and several different cancer types, including gastric, leukemia and breast cancer and is of great therapeutic interest.