ArticleiScience2023
ADMET property prediction via multi-task graph learning under adaptive auxiliary task selection.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- In Silico Design of Pyrimidine Derivatives as Potential α-Glucosidase Inhibitors: QSAR, Molecular Docking, ADMET, and Molecular Dynamics Studies.International journal of molecular sciences · 2026Article
- Artificial Intelligence Across the Drug Development Lifecycle.Medical sciences (Basel, Switzerland) · 2026Review
- Pharmacokinetic Prediction of Repurposed Drugs for PDAC Using Artificial Intelligence.ACS omega · 2026Article
- Computational identification of a marine derived dual inhibitor for type 2 diabetes mellitus using integrated in silico approaches.Scientific reports · 2026Article
- 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
- Targeting cannabinoid receptor 1 for multiple sclerosis: molecular docking and dynamic insights of berberine and curcumin as potential therapeutic agents.American journal of clinical and experimental immunology · 2026Article
- Structure based in silico screening of natural Vitamin D analogs for targeted and safer treatment of resistant multiple sclerosis.Scientific reports · 2025Article
- Discovery of Small Molecules Targeting Norovirus 3CL Protease by Multi-Stage Virtual Screening.International journal of molecular sciences · 2025Article
- MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction.Briefings in bioinformatics · 2024Article
- Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction.Chemical research in toxicology · 2024Review
- Integrating natural product research laboratory with artificial intelligence: Advancements and breakthroughs in traditional medicine.BioMedicine · 2024Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is a critical step in lead optimization to evaluate the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. Classical single-task learning (STL) has effectively predicted individual ADMET endpoints with abundant labels. Conversely, multi-task learning (MTL) can predict multiple ADMET endpoints with fewer labels, but ensuring task synergy and highlighting key molecular substructures remain challenges. To tackle these issues, this work elaborates a multi-task graph learning framework for predicting multiple ADMET properties of drug-like small molecules (MTGL-ADMET) by holding a new paradigm of MTL, "one primary, multiple auxiliaries." It first adeptly combines status theory with maximum flow for auxiliary task selection. The subsequent phase introduces a primary-task-centric MTL model with integrated modules. MTGL-ADMET not only outstrips existing STL and MTL methods but also offers a transparent lens into crucial molecular substructures. It is anticipated that this work can promote lead compound finding and optimization in drug discovery.
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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.