SIRT2i_Predictor: A Machine Learning-Based Tool to Facilitate the Discovery of Novel SIRT2 Inhibitors
Autori
Đoković, NemanjaRahnasto-Rilla, Minna
Lougiakis, Nikolas
Lahtela-Kakkonen, Maija
Nikolić, Katarina
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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. Benchmar...king 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.
Ključne reči:
classification / machine learning / Python GUI application / QSAR / regression / SIRT2 inhibitors / virtual screeningIzvor:
Pharmaceuticals, 2023, 16, 1Izdavač:
- MDPI
Finansiranje / projekti:
DOI: 10.3390/ph16010127
ISSN: 1424-8247
PubMed: 36678624
WoS: 000927677800001
Scopus: 2-s2.0-85146786280
Institucija/grupa
PharmacyTY - JOUR AU - Đoković, Nemanja AU - Rahnasto-Rilla, Minna AU - Lougiakis, Nikolas AU - Lahtela-Kakkonen, Maija AU - Nikolić, Katarina PY - 2023 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/4416 AB - 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. PB - MDPI T2 - Pharmaceuticals T1 - SIRT2i_Predictor: A Machine Learning-Based Tool to Facilitate the Discovery of Novel SIRT2 Inhibitors VL - 16 IS - 1 DO - 10.3390/ph16010127 ER -
@article{ author = "Đoković, Nemanja and Rahnasto-Rilla, Minna and Lougiakis, Nikolas and Lahtela-Kakkonen, Maija and Nikolić, Katarina", year = "2023", 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.", publisher = "MDPI", journal = "Pharmaceuticals", title = "SIRT2i_Predictor: A Machine Learning-Based Tool to Facilitate the Discovery of Novel SIRT2 Inhibitors", volume = "16", number = "1", doi = "10.3390/ph16010127" }
Đoković, N., Rahnasto-Rilla, M., Lougiakis, N., Lahtela-Kakkonen, M.,& Nikolić, K.. (2023). SIRT2i_Predictor: A Machine Learning-Based Tool to Facilitate the Discovery of Novel SIRT2 Inhibitors. in Pharmaceuticals MDPI., 16(1). https://doi.org/10.3390/ph16010127
Đoković N, Rahnasto-Rilla M, Lougiakis N, Lahtela-Kakkonen M, Nikolić K. SIRT2i_Predictor: A Machine Learning-Based Tool to Facilitate the Discovery of Novel SIRT2 Inhibitors. in Pharmaceuticals. 2023;16(1). doi:10.3390/ph16010127 .
Đoković, Nemanja, Rahnasto-Rilla, Minna, Lougiakis, Nikolas, Lahtela-Kakkonen, Maija, Nikolić, Katarina, "SIRT2i_Predictor: A Machine Learning-Based Tool to Facilitate the Discovery of Novel SIRT2 Inhibitors" in Pharmaceuticals, 16, no. 1 (2023), https://doi.org/10.3390/ph16010127 . .