NCBI PubMed Central
open access
A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
Checking your browser - reCAPTCHA Checking your browser before accessing pmc.ncbi.nlm.nih.gov ... Click here if you are not automatically redirected after 5 seconds.
Related documents
- A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer.#222942
- Development and Prospective Validation of a Pathology-Based Artificial Intelligence Model for Predicting the Time to Castration Resistance of Prostate Cancer#981736
- DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Network#798564
- DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Network#797151
- A review of machine learning in toxicology: current practices and reporting gaps.#382506
Record · ID 28280 · SHA-256 202202becb8dfccd
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive