AIMC Topic: Pharmacokinetics
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Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning.
Journal of pharmaceutical sciences
Jan 23, 2021
Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which ...
Bayer's in silico ADMET platform: a journey of machine learning over the past two decades.
Drug discovery today
Jul 9, 2020
Over the past two decades, an in silico absorption, distribution, metabolism, and excretion (ADMET) platform has been created at Bayer Pharma with the goal to generate models for a variety of pharmacokinetic and physicochemical endpoints in early dru...
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...
Machine Learning
Animals
Male
Protein Binding
Humans
Blood Proteins
Pharmaceutical Preparations
Dogs
Models, Biological
Rats
Rats, Sprague-Dawley
Liver
Metabolic Clearance Rate
Membranes, Artificial
Madin Darby Canine Kidney Cells
Permeability
Microsomes, Liver
Pharmacokinetics
Administration, Intravenous
Hepatocytes
Plasma
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors.
Artificial intelligence in medicine
Nov 27, 2019
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumors. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative infor...
Artificial intelligence and big data facilitated targeted drug discovery.
Stroke and vascular neurology
Nov 7, 2019
Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database in...
DrugMetab: An Integrated Machine Learning and Lexicon Mapping Named Entity Recognition Method for Drug Metabolite.
CPT: pharmacometrics & systems pharmacology
Sep 29, 2018
Drug metabolites (DMs) are critical in pharmacology research areas, such as drug metabolism pathways and drug-drug interactions. However, there is no terminology dictionary containing comprehensive drug metabolite names, and there is no named entity ...
In Silico Prediction of Major Clearance Pathways of Drugs among 9 Routes with Two-Step Support Vector Machines.
Pharmaceutical research
Aug 24, 2018
PURPOSE: The clearance pathways of drugs are critical elements for understanding the pharmacokinetics of drugs. We previously developed in silico systems to predict the five clearance pathway using a rectangular method and a support vector machine (S...
Artificial intelligence in drug design.
Science China. Life sciences
Jul 18, 2018
Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology, the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and devel...
Drug drug interaction extraction from the literature using a recursive neural network.
PloS one
Jan 26, 2018
Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI informati...
Connecting proteins with drug-like compounds: Open source drug discovery workflows with BindingDB and KNIME.
Database : the journal of biological databases and curation
Sep 16, 2015
Today's large, public databases of protein-small molecule interaction data are creating important new opportunities for data mining and integration. At the same time, new graphical user interface-based workflow tools offer facile alternatives to cust...
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