Target Fisher: A Consensus Structure‐Based Target Prediction Tool, and its Application in the Discovery of Selective MAO‐B Inhibitors
In this work we introduce Target Fisher, a consensus structure-based target prediction tool that integrates molecular docking and machine learning with the aim to aid in the identification of potential biological targets and the optimization of the use of bioassays. Target Fisher uses per-residue energy decomposition profiles extracted from docking poses as fingerprints to train target-specific machine learning models. It provides predictions for a curated set of 37 protein targets, covering a diverse range of biological entities, and offers a user-friendly interface accessible via a web server (https://gqc.quimica.unlp.edu.ar/targetfisher/). In this sense, Target Fisher is a valuable tool to aid organic and medicinal chemistry groups in target identification, drug discovery and drug repurposing. As a case study, we demonstrate the efficacy of Target Fisher by screening a small library of assorted natural products for targets relevant to neurodegenerative diseases, which resulted in the identification and experimental validation of selective inhibitors of monoamine oxidase B (MAO-B).
Authors
- Julián F. Fernández
- Rodrigo Peláez Suárez
- Francesco Caracciolo (ORCID: https://orcid.org/0000-0001-9430-7529)
- Lucía R. Fernández (ORCID: https://orcid.org/0000-0002-5978-4474)
- Marı́a Isabel Rodrı́guez-Franco (ORCID: https://orcid.org/0000-0002-6500-792X)
- Jorge A. Palermo (ORCID: https://orcid.org/0000-0002-0892-9389)
- Martín J. Lavecchia (ORCID: https://orcid.org/0000-0001-8678-4237)
- Gastón E. Siless (ORCID: https://orcid.org/0000-0001-5257-5314)
- Leandro Martínez Heredia
- Concepción Pérez (ORCID: https://orcid.org/0000-0001-5869-0495)
- Daniel Esses
Institutions
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Universidad de Buenos Aires (AR)
- Instituto de Química Médica (ES)
- Fundación Ciencias Exactas y Naturales (AR)
- Universidad Nacional de La Plata (AR)
Publication Details
- Journal
- Chemistry - A European Journal
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1002/chem.202401838
- Citations
- 2
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.72
Funders
- Consejo Nacional de Investigaciones Científicas y Técnicas
- Universidad de Buenos Aires
- Agencia Nacional de Promoción Científica y Tecnológica
- Secretaría de Ciencia y Técnica, Universidad de Buenos Aires
- Agencia Estatal de Investigación
- Fondo para la Investigación Científica y Tecnológica