ArticleACS omega2026
Pharmacokinetic Prediction of Repurposed Drugs for PDAC Using Artificial Intelligence.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer that accounts for 95% of cases of pancreatic cancer. It develops in the ducts and shows high drug resistance. In this study, we proposed a framework to predict the pharmacokinetic (PK) properties of repurposed drugs for PDAC using artificial intelligence (AI). Initially, the molecular features of repurposable drugs for PDAC were generated through three types of molecular descriptors: RDKit, MACCS, and ECFP6. Then, the corresponding absorption (Caco-2 cell permeability), distribution (volume of distribution), metabolism (CYP2C9 inhibitor), excretion (half-life), and toxicity (hERG) properties of the drugs were obtained from ADMETlab 3.0. We constructed AI models such as multilayer perceptron (MLP), random forest (RF), extreme gradient boosting (XGB), and one-dimensional convolutional neural network with different combinations of molecular descriptors as the input. The performance of the models was evaluated on an open-access data set, Therapeutics Data Commons (TDC), and using evaluation metrics. Our results show that the highest-performing molecular descriptor combination and AI models vary with respect to the PK properties. Models on the PDAC data set achieved a mean absolute error (MAE) of 0.18 (MACCS+XGB), Spearman correlation (SC) of 0.39 (MACCS+RF), area under the precision-recall curve (AUPRC) of 59.44% (MACCS+ECFP6+MLP), SC of 0.68 (RDKit+ECFP6+XGB), and SC of 0.77 (MACCS+RF) for absorption, distribution, metabolism, excretion, and toxicity, respectively. The corresponding values on the TDC data set are an MAE of 0.26 (RDKit+MACCS+MLP), an SC of 0.62 (MACCS+ECFP6+MLP), an AUPRC of 67.11% (RDKit+MACCS+ECFP6+1D-CNN), an SC of 0.39 (MACCS+RF), and an SC of 0.92 (RDKit+XGB/RDKit+MACCS+RF). These results suggest that combining molecular fingerprints with AI can effectively model PK properties. This approach supports the use of AI for accelerating drug repurposing, especially for disease conditions.
Identifiers
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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 Socratic graph.