AIMC Topic: Microsomes, Liver
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Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.
Journal of chemical information and modeling
Jul 8, 2024
In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of co...
In vitro metabolic studies and machine learning analysis of mass spectrometry data: A dual strategy for differentiating alpha-pyrrolidinohexiophenone (α-PHP) and alpha-pyrrolidinoisohexanophenone (α-PiHP) in urine analysis.
Forensic science international
Jul 6, 2024
Synthetic cathinones are some of the most prevalent new psychoactive substances (NPSs) globally, with alpha-pyrrolidinoisohexanophenone (α-PiHP) being particularly noted for its widespread use in the United States, Europe, and Taiwan. However, the an...
Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence.
Journal of chemical information and modeling
Mar 18, 2024
Liver microsomal stability, a crucial aspect of metabolic stability, significantly impacts practical drug discovery. However, current models for predicting liver microsomal stability are based on limited molecular information from a single species. T...
Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition.
Journal of chemical information and modeling
Oct 21, 2023
Cytochrome P450 (CYP) is a family of enzymes that are responsible for about 75% of all metabolic reactions. Among them, CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 participate in the metabolism of most drugs and mediate many adverse drug reactions. T...
In Silico Prediction of Human and Rat Liver Microsomal Stability via Machine Learning Methods.
Chemical research in toxicology
Sep 2, 2022
Liver microsomal stability is an important property considered for the screening of drug candidates in the early stage of drug development. Determination of hepatic metabolic stability can be performed by an in vitro assay, but it requires quite a fe...
Human and rat microsomal metabolites of N-tert-butoxycarbonylmethamphetamine and its urinary metabolites in rat.
Forensic toxicology
Aug 24, 2021
PURPOSE: N-tert-Butoxycarbonylmethamphetamine (BocMA), a masked derivative of methamphetamine (MA), converts into MA under acidic condition and potentially acts as a precursor to MA following ingestion. To investigate the metabolism and excretion of ...
Transfer learning enables prediction of CYP2D6 haplotype function.
PLoS computational biology
Nov 2, 2020
Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene whose protein product metabolizes more than 20% of clinically used drugs. Genetic variations in CYP2D6 are responsible for interindividual heterogeneity in drug response that can lead to drug ...
Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay.
Molecular pharmaceutics
Jun 12, 2020
The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CL) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descrip...
Rats
Rats, Sprague-Dawley
Liver
Dogs
Models, Biological
Blood Proteins
Pharmaceutical Preparations
Machine Learning
Animals
Male
Metabolic Clearance Rate
Protein Binding
Membranes, Artificial
Permeability
Humans
Pharmacokinetics
Administration, Intravenous
Plasma
Madin Darby Canine Kidney Cells
Microsomes, Liver
Hepatocytes
The Metabolic Rainbow: Deep Learning Phase I Metabolism in Five Colors.
Journal of chemical information and modeling
Feb 24, 2020
Metabolism of drugs affects their absorption, distribution, efficacy, excretion, and toxicity profiles. Metabolism is routinely assessed experimentally using recombinant enzymes, human liver microsome, and animal models. Unfortunately, these experime...
Deep learning enables rapid identification of potent DDR1 kinase inhibitors.
Nature biotechnology
Sep 2, 2019
We have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility, novelty, and biological activity. We used GENTRL to discover potent inhibitors...
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