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QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction

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    SYSNO ASEP0538137
    Druh ASEPJ - Článek v odborném periodiku
    Zařazení RIVJ - Článek v odborném periodiku
    Poddruh JČlánek ve WOS
    NázevQSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction
    Tvůrce(i) Cortes-Ciriano, I. (GB)
    Škuta, Ctibor (UMG-J)
    Bender, A. (GB)
    Svozil, Daniel (UMG-J)
    Celkový počet autorů4
    Číslo článku41
    Zdroj.dok.Journal of Cheminformatics. - : Chemistry Central - ISSN 1758-2946
    Roč. 12, č. 1 (2020)
    Poč.str.17 s.
    Forma vydáníOnline - E
    Jazyk dok.eng - angličtina
    Země vyd.GB - Velká Británie
    Klíč. slovaqsar ; Affinity fingerprints ; ChEMBL ; Bioactivity modeling ; Cytotoxicity ; Drug sensitivity prediction ; Drug sensitivity
    Vědní obor RIVEB - Genetika a molekulární biologie
    Obor OECDComputer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    CEPLM2015063 GA MŠk - Ministerstvo školství, mládeže a tělovýchovy
    Způsob publikováníOpen access
    Institucionální podporaUMG-J - RVO:68378050
    UT WOS000549151400001
    DOI10.1186/s13321-020-00444-5
    AnotaceAffinity fingerprints report the activity of small molecules across a set of assays, and thus permit to gather information about the bioactivities of structurally dissimilar compounds, where models based on chemical structure alone are often limited, and model complex biological endpoints, such as human toxicity and in vitro cancer cell line sensitivity. Here, we propose to model in vitro compound activity using computationally predicted bioactivity profiles as compound descriptors. To this aim, we apply and validate a framework for the calculation of QSAR-derived affinity fingerprints (QAFFP) using a set of 1360 QSAR models generated using K-i, K-d, IC50 and EC50 data from ChEMBL database. QAFFP thus represent a method to encode and relate compounds on the basis of their similarity in bioactivity space. To benchmark the predictive power of QAFFP we assembled IC50 data from ChEMBL database for 18 diverse cancer cell lines widely used in preclinical drug discovery, and 25 diverse protein target data sets. This study complements part 1 where the performance of QAFFP in similarity searching, scaffold hopping, and bioactivity classification is evaluated. Despite being inherently noisy, we show that using QAFFP as descriptors leads to errors in prediction on the test set in the similar to 0.65-0.95 pIC(50) units range, which are comparable to the estimated uncertainty of bioactivity data in ChEMBL (0.76-1.00 pIC(50) units). We find that the predictive power of QAFFP is slightly worse than that of Morgan2 fingerprints and 1D and 2D physicochemical descriptors, with an effect size in the 0.02-0.08 pIC(50) units range. Including QSAR models with low predictive power in the generation of QAFFP does not lead to improved predictive power. Given that the QSAR models we used to compute the QAFFP were selected on the basis of data availability alone, we anticipate better modeling results for QAFFP generated using more diverse and biologically meaningful targets. Data sets and Python code are publicly available at.
    PracovištěÚstav molekulární genetiky
    KontaktGabriela Marešová, maresova@img.cas.cz, Tel.: 241 063 217
    Rok sběru2021
    Elektronická adresahttps://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00444-5
Počet záznamů: 1