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Open AccessArticle

Accelerating Drug Discovery by Early Protein Drug Target Prediction Based on a Multi-Fingerprint Similarity Search

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Dipartimento di Farmacia—Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, via E. Orabona, 4, I-70125 Bari, Italy
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Cineca, Via Magnanelli 6/3, 40033 Casalecchio di Reno, Bologna, Italy
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Dipartimento di Chimica, Universitaà degli Studi di Bari “Aldo Moro”, via E. Orabona, 4, I-70125 Bari, Italy
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Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156 Milano, Italy
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Istituto di Cristallografia, Consiglio Nazionale delle Ricerche, Via G. Amendola 122/O, 70126 Bari, Italy
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Author to whom correspondence should be addressed.
In loving memory of Michele Montaruli.
Michele Montaruli passed away on 24 March 2019.
Academic Editor: Alessandro Pedretti
Molecules 2019, 24(12), 2233; https://doi.org/10.3390/molecules24122233
Received: 11 February 2019 / Revised: 8 June 2019 / Accepted: 12 June 2019 / Published: 14 June 2019
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and the exploration of a larger data domain, which included more than half a million high-quality small molecules extracted from the latest release of ChEMBL (version 24.1, at the time of writing). Provided with a high biological assay confidence score, these selected compounds explored up to 2822 protein drug targets. To improve the data accuracy, samples marked as prodrugs or with equivocal biological annotations were not considered. Notably, MuSSel performances were overall improved by using an object-relational database management system based on PostgreSQL. In order to challenge the real effectiveness of MuSSel in predicting relevant therapeutic drug targets, we analyzed a pool of 36 external bioactive compounds published in the Journal of Medicinal Chemistry from October to December 2018. This study demonstrates that the use of highly curated chemical and biological experimental data on one side, and a powerful multi-fingerprint search algorithm on the other, can be of the utmost importance in addressing the fate of newly conceived small molecules, by strongly reducing the attrition of early phases of drug discovery programs. View Full-Text
Keywords: molecular similarity; multi-fingerprint; data quality; protein drug target prediction molecular similarity; multi-fingerprint; data quality; protein drug target prediction
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MDPI and ACS Style

Montaruli, M.; Alberga, D.; Ciriaco, F.; Trisciuzzi, D.; Tondo, A.R.; Mangiatordi, G.F.; Nicolotti, O. Accelerating Drug Discovery by Early Protein Drug Target Prediction Based on a Multi-Fingerprint Similarity Search . Molecules 2019, 24, 2233.

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