ArticleBiomolecules2022
miDruglikeness: Subdivisional Drug-Likeness Prediction Models Using Active Ensemble Learning Strategies.
Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Exploration of network-based, machine learning, MD simulations, and MM/GBSA approaches revealed luteolin from Gynura procumbens as key inhibitor of MMP9 in NSCLC.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Resveratrol Activates SIRT1 to Inhibit Trophoblast Pyroptosis in Preeclampsia.Reproductive sciences (Thousand Oaks, Calif.) · 2025Article
- DrugMetric: quantitative drug-likeness scoring based on chemical space distance.Briefings in bioinformatics · 2024Article
- DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles.Journal of cheminformatics · 2024Article
- Insilico exploration C. koseri ATP synthase inhibitors by pharmacophore-based virtual screening, molecular docking and MD simulation.PloS one · 2024Article
- Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence-II.Biomolecules · 2023Article
- Current Trends and Challenges in Drug-Likeness Prediction: Are They Generalizable and Interpretable?Health data science · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural networks, we developed three subdivisional models to individually predict the capacity of a compound to enter in vivo testing, clinical trials, and market approval stages. Furthermore, we proposed a strategy combing both active learning and ensemble learning to improve the quality of the models. The models achieved satisfactory performance in the internal test datasets and four self-collected external test datasets. We also employed the models as a general index to make an evaluation on a widely known benchmark dataset DEKOIS 2.0, and surprisingly found a powerful ability on virtual screening tasks. Our model system (termed as miDruglikeness) provides a comprehensive drug-likeness prediction tool for drug discovery and development.
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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.