Probabilistic Predictive Model of the Human Liver Microsomal Metabolism Regioselectivity
Poster Dec 07, 2010
Justas Dapkunas, Andrius Sazonovas, Pranas Japertas
Moreover, in silico predictions of the most likely metabolism sites in a molecule could facilitate the
analysis of spectroscopic data and thus ease the experimental identification of metabolites.
In this work, we present QSAR models for the prediction of metabolism regioselectivity. They provide the probability to be metabolized in human liver microsomes for every atom of the molecule and are based on a novel GALAS (Global, Adjusted Locally According to Similarity) methodology – an approach enabling the evaluation of Model Applicability Domain via the calculation of the prediction Reliability Index (RI).
Novel lead optimization strategy of BACE I inhibitors for the treatment of Alzheimer’s disease by Quantitative Structure-Activity Relationship (QSAR) and Physiologically-Based Pharmacokinetics (PBPK) modelingPoster
In this study, we introduce an innovative in silico-based high throughput lead optimization strategy with QSAR and PBPK modelings using StarDrop™, ADMET predictor® and GastroPlus®.READ MORE
RAMclust/RAMsearch: efficient post-XCMS feature clustering and annotation of MS-based metabolomics datasetsPoster
Chromatographically coupled mass spectrometry is a powerful tool for profiling, semi-quantitatively or quantitatively, a breadth of small molecules with sensitivity and selectivity.READ MORE
A Novel Benchtop Time-of-Flight GC-MS System for High Performance Analysis of Human UrinePoster
A workflow was developed and implemented for the effective comparison of urine. It involved the preparation of stable compounds through derivatization, data acquisition using a high performance benchtop TOFMS, comprehensive data processing (NonTarget Deconvolution™), and quick retrospective analysis of rich datasets using Target Analyte Finding.
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