Rational design of SIRT2 inhibitors: Integrating 3D-QSAR, molecular docking and machine learning techniques

Abstract

SIRT2 influences processes such as apoptosis, DNA repair, and the cell cycle, making it an attractive target for therapeutic intervention in cancer treatment. Although potent inhibitors have been developed, the main challenge remains the design of highly selective SIRT2 inhibitors, especially due to the structural similarity of the NAD+ binding site across other members of the sirtuin family. Currently, derivatives of 5 ((3-amidobenzyl)oxy)nicotinamides stand out as some of the most selective and effective SIRT2 inhibitors, paving the way for further research toward the development of new therapeutically relevant molecules. [1] In this study, we developed a 3D-Quantitative Structure-Activity Relationship (3D-QSAR) model based on a dataset of 86 nicotinamide-based SIRT2 inhibitors complemented by GRIND-derived pharmacophore models. [2] External validation confirmed the reliability of the 3D-QSAR model in predicting SIRT2 inhibition within the defined range of application. [3] The model interpretation enabled the design of novel SIRT2 inhibitors. Furthermore, based on molecular docking results for the SIRT1–3 isoforms, we developed two classification machine learning models to predict the selectivity of inhibitors for the SIRT1/2 and SIRT2/3 isoforms, which was confirmed by external validation. The integration of 3D-QSAR, selectivity modeling and ADMET predictions facilitated the identification of promising selective SIRT2 inhibitors (Figure 1). These in silico identified inhibitors will be synthesized and tested in vitro to confirm their efficacy and selectivity and pave the way for novel SIRT2-targeted therapies for cancer and neurodegenerative diseases.33rd Annual GP2A Medicinal Chemistry Conference, XIVth Paul Ehrlich MedChem Euro-PhD Network Meeting & COST Action OneHealthdrugs, Abstract Book Nantes Université – France, 11th – 13th June 202