ArticleNucleic acids research2024
Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
129 citing papers in PubMed, 1 synthesis or guideline pooled it, 208 citations in OpenAlex.
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- Benzamides of 2-(aminophenyl)benzimidazoles and 2-(aminophenyl)indoles as Anticancer Scaffolds: Synthesis, In Silico and In Vitro Evaluation.Pharmaceuticals (Basel, Switzerland) · 2026Article
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- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
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- Pyridyl-Thiazole-Thiosemicarbazones as Antitrypanosomatidae Agents: Design, Synthesis, and In Silico Profiling.ChemMedChem · 2026Article
- Consensus In Silico Pharmacodynamic Screening of Twelve Fabaceae Isoflavones Targeting Glycation, Oxidative Stress, Antidiabetic Activity, and NF-κB-Related Inflammation.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Rational design of a lipophilic β-galactosidase-responsive near-infrared probe for in vivo imaging of cellular senescence.Smart molecules : open access · 2026Article
- Deep learning-assisted virtual screening of a large chemical library for selective GSK3β inhibitors.Molecular diversity · 2026Article
- Identification of hypertension-associated bacterial key genes as potential targets and therapeutic agents through integrated bioinformatics approach.International microbiology : the official journal of the Spanish Society for Microbiology · 2026Article
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- First structural Elucidation of Bcl-2 functional conversion induced by validated modulators using microsecond-scale molecular dynamics simulations and density functional theory.Molecular diversity · 2026Article
69 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
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 from four perspectives: absorption, distribution, metabolism and excretion-all of which are closely related to a fifth perspective, toxicity (ADMET). Since obtaining ADMET data from in vitro, in vivo or pre-clinical stages is time consuming and expensive, many efforts have been made to predict ADMET properties via computational approaches. However, the majority of available methods are limited in their ability to provide pharmacokinetics and toxicity for diverse targets, ensure good overall accuracy, and offer ease of use, interpretability and extensibility for further optimizations. Here, we introduce Deep-PK, a deep learning-based pharmacokinetic and toxicity prediction, analysis and optimization platform. We applied graph neural networks and graph-based signatures as a graph-level feature to yield the best predictive performance across 73 endpoints, including 64 ADMET and 9 general properties. With these powerful models, Deep-PK supports molecular optimization and interpretation, aiding users in optimizing and understanding pharmacokinetics and toxicity for given input molecules. The Deep-PK is freely available at https://biosig.lab.uq.edu.au/deeppk/.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.