SIRT2i_Predictor: A Machine Learning-Based Tool to Facilitate the Discovery of Novel SIRT2 Inhibitors

Abstract

A growing body of preclinical evidence recognized selective sirtuin 2 (SIRT2) inhibitors as novel therapeutics for treatment of age-related diseases. However, none of the SIRT2 inhibitors have reached clinical trials yet. Transformative potential of machine learning (ML) in early stages of drug discovery has been witnessed by widespread adoption of these techniques in recent years. Despite great potential, there is a lack of robust and large-scale ML models for discovery of novel SIRT2 inhibitors. In order to support virtual screening (VS), lead optimization, or facilitate the selection of SIRT2 inhibitors for experimental evaluation, a machine-learning-based tool titled SIRT2i_Predictor was developed. The tool was built on a panel of high-quality ML regression and classification-based models for prediction of inhibitor potency and SIRT1-3 isoform selectivity. State-of-the-art ML algorithms were used to train the models on a large and diverse dataset containing 1797 compounds. Benchmarking against structure-based VS protocol indicated comparable coverage of chemical space with great gain in speed. The tool was applied to screen the in-house database of compounds, corroborating the utility in the prioritization of compounds for costly in vitro screening campaigns. The easy-to-use web-based interface makes SIRT2i_Predictor a convenient tool for the wider community. The SIRT2i_Predictor’s source code is made available online.

Recent Advances in the Discovery of SIRT1/2 Inhibitors via Computational Methods: A Perspective

“…Along with this, the machine-learning-based tool, namely SIRT2i_Predictor, has been developed, thus providing further support for the conventional VS calculations and for the lead optimization process [160]. This was proposed based on a panel of machine-learning regression and classification-based models to predict ligand potency and selectivity toward SIRT1-3.…”

Age-Associated Changes of Sirtuin 2 Expression in CNS and the Periphery

“…In addition to these molecules, other strategies have also been used to inhibit SIRT2 activity, such as peptides (YKK(ε-thioAc)AM) [107] or microRNAs (miR-212-5p) [78]. Moreover, a new machine-learning-based tool has been recently developed by Djokovic and colleagues [108]. This tool, called SIRT2i_Predictor, is able to predict which molecules could be selective and potent SIRT2 inhibitors, prioritizing the best compounds and reducing the time and cost of developing novel inhibitors [108].…”

“…Moreover, a new machine-learning-based tool has been recently developed by Djokovic and colleagues [108]. This tool, called SIRT2i_Predictor, is able to predict which molecules could be selective and potent SIRT2 inhibitors, prioritizing the best compounds and reducing the time and cost of developing novel inhibitors [108].…”