J Chem Inf Model - Using information from historical high-throughput screens to predict active compounds.

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Resumo

Modern high-throughput screening (HTS) is a well-established approach for hit finding in drug discovery that is routinely employed in the pharmaceutical industry to screen more than a million compounds within a few weeks. However, as the industry shifts to more disease-relevant but more complex phenotypic screens, the focus has moved to piloting smaller but smarter chemically/biologically diverse subsets followed by an expansion around hit compounds. One standard method for doing this is to train a machine-learning (ML) model with the chemical fingerprints of the tested subset of molecules and then select the next compounds based on the predictions of this model. An alternative approach would be to take advantage of the wealth of bioactivity information contained in older (full-deck) screens using so-called HTS fingerprints, where each element of the fingerprint corresponds to the outcome of a particular assay, as input to machine-learning algorithms. We constructed HTS fingerprints using two collections of data: 93 in-house assays and 95 publicly available assays from PubChem. For each source, an additional set of 51 and 46 assays, respectively, was collected for testing. Three different ML methods, random forest (RF), logistic regression (LR), and na?ve Bayes (NB), were investigated for both the HTS fingerprint and a chemical fingerprint, Morgan2. RF was found to be best suited for learning from HTS fingerprints yielding area under the receiver operating characteristic curve (AUC) values >0.8 for 78% of the internal assays and enrichment factors at 5% (EF(5%)) >10 for 55% of the assays. The RF(HTS-fp) generally outperformed the LR trained with Morgan2, which was the best ML method for the chemical fingerprint, for the majority of assays. In addition, HTS fingerprints were found to retrieve more diverse chemotypes. Combining the two models through heterogeneous classifier fusion led to a similar or better performance than the best individual model for all assays. Further validation using a pair of in-house assays and data from a confirmatory screen--including a prospective set of around 2000 compounds selected based on our approach--confirmed the good performance. Thus, the combination of machine-learning with HTS fingerprints and chemical fingerprints utilizes information from both domains and presents a very promising approach for hit expansion, leading to more hits. The source code used with the public data is provided.

Resumo Limpo

modern highthroughput screen hts wellestablish approach hit find drug discoveri routin employ pharmaceut industri screen million compound within week howev industri shift diseaserelev complex phenotyp screen focus move pilot smaller smarter chemicallybiolog divers subset follow expans around hit compound one standard method train machinelearn ml model chemic fingerprint test subset molecul select next compound base predict model altern approach take advantag wealth bioactiv inform contain older fulldeck screen use socal hts fingerprint element fingerprint correspond outcom particular assay input machinelearn algorithm construct hts fingerprint use two collect data inhous assay public avail assay pubchem sourc addit set assay respect collect test three differ ml method random forest rf logist regress lr nave bay nb investig hts fingerprint chemic fingerprint morgan rf found best suit learn hts fingerprint yield area receiv oper characterist curv auc valu intern assay enrich factor ef assay rfhtsfp general outperform lr train morgan best ml method chemic fingerprint major assay addit hts fingerprint found retriev divers chemotyp combin two model heterogen classifi fusion led similar better perform best individu model assay valid use pair inhous assay data confirmatori screeninclud prospect set around compound select base approachconfirm good perform thus combin machinelearn hts fingerprint chemic fingerprint util inform domain present promis approach hit expans lead hit sourc code use public data provid

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