Beam Search for Automated Design and Scoring of Novel ROR Ligands with Machine Intelligence*.
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ABSTRACT:
SUBMITTER: Moret M
PROVIDER: S-EPMC8457062 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In this work, we developed and applied a computational procedure for creating and validating predictive models capable of estimating the biological activity of ligands. The combination of modern machine learning methods, experimental data, and the appropriate setup of molecular descriptors led to a set of well-performing models. We thoroughly inspected both the methodological space and various possibilities for creating a chemical feature space. The resulting models were applied to the virtual screening of the ZINC20 database to identify new, biologically active ligands of RORγ receptors, which are a subfamily of nuclear receptors. Based on the known ligands of RORγ, we selected candidates and calculate their predicted activities with the best-performing models. We chose two candidates that were experimentally verified. One of these candidates was confirmed to induce the biological activity of the RORγ receptors, which we consider proof of the efficacy of the proposed methodology.
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Project description:ObjectivesThe objective of this study was to assess the feasibility and accuracy of a fully automated artificial intelligence (AI) powered coronary artery calcium scoring (CACS) method on ungated CT in oncologic patients undergoing 18F-FDG PET/CT.MethodsA total of 100 oncologic patients examined between 2007 and 2015 were retrospectively included. All patients underwent 18F-FDG PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on non-contrast ECG-gated CT scans obtained from SPECT-MPI (i.e., reference standard). Additionally, CACS was performed using a cloud-based, user-independent tool (AI-CACS) on ungated CT scans from 18F-FDG-PET/CT examinations. Agatston scores from the manual CACS and AI-CACS were compared.ResultsOn a per-patient basis, the AI-CACS tool achieved a sensitivity and specificity of 85% and 90% for the detection of CAC. Interscore agreement of CACS between manual CACS and AI-CACS was 0.88 (95% CI: 0.827, 0.918). Interclass agreement of risk categories was 0.8 in weighted Kappa analysis, with a reclassification rate of 44% and an underestimation of one risk category by AI-CACS in 39% of cases. On a per-vessel basis, interscore agreement of CAC scores ranged from 0.716 for the circumflex artery to 0.863 for the left anterior descending artery.ConclusionsFully automated AI-CACS as performed on non-contrast free-breathing, ungated CT scans from 18F-FDG-PET/CT examinations is feasible and provides an acceptable to good estimation of CAC burden. CAC load on ungated CT is, however, generally underestimated by AI-CACS, which should be taken into account when interpreting imaging findings.
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