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      Multiple Targets, One Goal: Compounding life-extending effects through Polypharmacology

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          Abstract

          Analysis of lifespan-extending compounds suggested the most effective geroprotectors target multiple biogenic amine receptors. To test this hypothesis, we used graph neural networks to predict such polypharmacological compounds and evaluated them in C. elegans. Over 70% of the selected compounds extended lifespan, with effect sizes in the top 5% compared to the DrugAge database. This reveals that rationally designing polypharmacological compounds enables the design of geroprotectors with exceptional efficacy.

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          MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets

          Martin Steinegger, Johannes Söding (2017)
          Article 0 comments Cited 1002 times – based on 0 reviews     Review now
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            The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

            Davide Chicco, Giuseppe Jurman (2020)
            Background To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. Results The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. Conclusions In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F1 score in evaluating binary classification tasks by all scientific communities.
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              DrugBank: a comprehensive resource for in silico drug discovery and exploration

              David Wishart, Craig Knox, An Guo … (2005)
              DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug (i.e. chemical) data with comprehensive drug target (i.e. protein) information. The database contains >4100 drug entries including >800 FDA approved small molecule and biotech drugs as well as >3200 experimental drugs. Additionally, >14 000 protein or drug target sequences are linked to these drug entries. Each DrugCard entry contains >80 data fields with half of the information being devoted to drug/chemical data and the other half devoted to drug target or protein data. Many data fields are hyperlinked to other databases (KEGG, PubChem, ChEBI, PDB, Swiss-Prot and GenBank) and a variety of structure viewing applets. The database is fully searchable supporting extensive text, sequence, chemical structure and relational query searches. Potential applications of DrugBank include in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. DrugBank is available at .
              Article 0 comments Cited 545 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): bioRxiv
                Journal ID (publisher-id): BIORXIV
                Title: bioRxiv
                Publisher: Cold Spring Harbor Laboratory
                ISSN (Electronic): 2692-8205
                Publication date (Electronic): 28 June 2024
                Electronic Location Identifier: 2024.06.23.600269
                Affiliations
                [1 ]Gero PTE, 60 Paya Lebar Road # 05-40B, Paya Lebar Square, 409051, Singapore
                [2 ]Department of Molecular and Cellular Biology, Molecular Medicine and Neuroscience, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla, CA 92037
                Author notes
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                co-first authors

                Author information
                http://orcid.org/0000-0001-8359-3689
                http://orcid.org/0000-0003-1381-5295
                http://orcid.org/0000-0002-1010-145X
                http://orcid.org/0000-0003-0404-808X
                Article
                DOI: 10.1101/2024.06.23.600269
                PMC ID: 11230182
                PubMed ID: 38979167
                SO-VID: 58719e06-edc4-44a9-aded-dc77d657cec0
                License:

                This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.

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