AIMC Topic: Pharmacokinetics
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Boosting drug named entity recognition using an aggregate classifier.
Artificial intelligence in medicine
Jun 17, 2015
OBJECTIVE: Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost al...
Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools.
Advanced drug delivery reviews
May 30, 2015
In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanis...
Prediction of plasma concentration-time profiles in mice using deep neural network integrated with pharmacokinetic models.
International journal of pharmaceutics
May 15, 2025
Quantitative structure-activity relationship (QSAR) methods have emerged as powerful tools to streamline non-clinical pharmacokinetic (PK) studies, with extensive evidence demonstrating their potential to predict key in vivo PK parameters such as cle...
Predicting Pharmacokinetics in Rats Using Machine Learning: A Comparative Study Between Empirical, Compartmental, and PBPK-Based Approaches.
Clinical and translational science
Mar 1, 2025
A successful drug needs to combine several properties including high potency and good pharmacokinetic (PK) properties to sustain efficacious plasma concentration over time. To estimate required doses for preclinical animal efficacy models or for the ...
Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction.
Nucleic acids research
Jul 5, 2024
Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate of drugs in the human body, are described fro...
Machine learning framework to predict pharmacokinetic profile of small molecule drugs based on chemical structure.
Clinical and translational science
May 1, 2024
Accurate prediction of a new compound's pharmacokinetic (PK) profile is pivotal for the success of drug discovery programs. An initial assessment of PK in preclinical species and humans is typically performed through allometric scaling and mathematic...
Improving the accuracy and convergence of drug permeation simulations via machine-learned collective variables.
The Journal of chemical physics
Jul 28, 2021
Understanding the permeation of biomolecules through cellular membranes is critical for many biotechnological applications, including targeted drug delivery, pathogen detection, and the development of new antibiotics. To this end, computer simulation...
Predicting drug-disease associations through layer attention graph convolutional network.
Briefings in bioinformatics
Jul 20, 2021
BACKGROUND: Determining drug-disease associations is an integral part in the process of drug development. However, the identification of drug-disease associations through wet experiments is costly and inefficient. Hence, the development of efficient ...
Opportunities and challenges using artificial intelligence in ADME/Tox.
Nature materials
May 1, 2019
A recent conference organized a panel of scientists representing small and big pharma companies, who work at the interface of machine learning (ML) and absorption, distribution, metabolism, excretion, and toxicology (ADME/Tox). With the recent rebirt...
SuperDRUG2: a one stop resource for approved/marketed drugs.
Nucleic acids research
Jan 4, 2018
Regular monitoring of drug regulatory agency web sites and similar resources for information on new drug approvals and changes to legal status of marketed drugs is impractical. It requires navigation through several resources to find complete informa...
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