ArticleScientific data2024
PharmaBench: Enhancing ADMET benchmarks with large language models.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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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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Who cites it
24 citing papers in PubMed.
- Gaps in AI-Driven Pharmacokinetic Property Prediction for Early Drug Development: A Scoping Review.Journal of chemical information and modeling · 2026Article
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- An End-User Audit of Reproducibility, Data Leakage, and Overfitting of the Top-Ranked ADMET Prediction Models in TDC Leaderboards.Journal of chemical information and modeling · 2026Article
- ADMETPred: a high-throughput ADMET prediction platform integrating multi-model algorithms and interpretable substructure identification.Science China. Life sciences · 2026Article
- Construction of a curated human pharmacokinetics database for molecular fragment analysis and machine learning applications.Drug metabolism and disposition: the biological fate of chemicals · 2026Article
- Antiviral Activity of Polyene Macrolides Against Newcastle Disease Virus: Computational and Experimental Insights.Molecules (Basel, Switzerland) · 2026Article
- Revisiting ADMET prediction reliability under real-world challenges in the foundation model era.Journal of cheminformatics · 2026Article
- Precision oncology in the age of AI: lessons from AI-driven drug discovery and clinical translation.BJC reports · 2026Review
- Data curation in cheminformatics: importance and implementation.Journal of cheminformatics · 2026Review
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application.Drug metabolism and disposition: the biological fate of chemicals · 2026Review
- NPASS database update 2026: comprehensive quantitative composition, bioactivity, and ADME-Tox data of natural products for biomedical research.Nucleic acids research · 2026Article
- Machine learning-based unified models for predicting drug clearance from pharmacokinetic animal and study design variables.PloS one · 2026Article
- Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- Artificial intelligence in drug development for delirium and Alzheimer's disease.Acta pharmaceutica Sinica. B · 2025Review
- Pharmacological Overview of Bioactive Natural Products fromPlants (Basel, Switzerland) · 2025Review
- Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction.Briefings in bioinformatics · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Accurately predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties early in drug development is essential for selecting compounds with optimal pharmacokinetics and minimal toxicity. Existing ADMET-related benchmark sets are limited in utility due to their small dataset sizes and the lack of representation of compounds used in drug discovery projects. These shortcomings hinder their application in model building for drug discovery. To address this issue, we propose a multi-agent data mining system based on Large Language Models that effectively identifies experimental conditions within 14,401 bioassays. This approach facilitates merging entries from different sources, culminating in the creation of PharmaBench. Additionally, we have developed a data processing workflow to integrate data from various sources, resulting in 156,618 raw entries. Through this workflow, we constructed PharmaBench, a comprehensive benchmark set for ADMET properties, which comprises eleven ADMET datasets and 52,482 entries. This benchmark set is designed to serve as an open-source dataset for the development of AI models relevant to drug discovery projects.
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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 OpenQuestion graph.