ArticleChemical science2022
Drug-likeness scoring based on unsupervised learning.
Article in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Article
- Design, synthesis and biological evaluation of 2,5-diaryloxazolo[4,5-Beilstein journal of organic chemistry · 2026Article
- ADME-drug-likeness: enriching molecular foundation models via pharmacokinetics-guided multi-task learning for drug-likeness prediction.Bioinformatics (Oxford, England) · 2025Article
- Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.Journal of medicinal chemistry · 2025Review
- Synthesis, DFT, ADMET, and Docking Studies of Novel Sulfonyl Piperidine Analogues Containing 2,3-dihydrobenzofuran-5-carboxamide.Current medicinal chemistry · 2025Article
- Development of Drug-Induced Gene Expression Ranking Analysis (DIGERA) and Its Application to Virtual Screening for Poly (ADP-Ribose) Polymerase 1 Inhibitor.International journal of molecular sciences · 2024Article
- Molecular similarity: Theory, applications, and perspectives.Artificial intelligence chemistry · 2024Article
- Fungal secondary metabolites as a potential inhibitor of T315I- BCR::ABL1 mutant in chronic myeloid leukemia by molecular docking, molecular dynamics simulation and binding free energy exploration approaches.Journal, genetic engineering & biotechnology · 2024Article
- Unraveling Cordia myxa's anti-malarial potential: integrative insights from network pharmacology, molecular modeling, and machine learning.BMC infectious diseases · 2024Article
- ExploringHeliyon · 2024Article
- Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach.Cancer medicine · 2024Article
- DrugMetric: quantitative drug-likeness scoring based on chemical space distance.Briefings in bioinformatics · 2024Article
- Bidirectional generation of structure and properties through a single molecular foundation model.Nature communications · 2024Article
- DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles.Journal of cheminformatics · 2024Article
- Using alternative SMILES representations to identify novel functional analogues in chemical similarity vector searches.Patterns (New York, N.Y.) · 2023Article
- Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction.Journal of chemical information and modeling · 2023Article
- MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules.Journal of cheminformatics · 2023Article
- Current Trends and Challenges in Drug-Likeness Prediction: Are They Generalizable and Interpretable?Health data science · 2023Article
- miDruglikeness: Subdivisional Drug-Likeness Prediction Models Using Active Ensemble Learning Strategies.Biomolecules · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite data for regression is available. Recently, binary classification models based on graph neural networks have been proposed but with strong dependency of their performances on the choice of the negative set for training. Here we propose a novel unsupervised learning model that requires only known drugs for training. We adopted a language model based on a recurrent neural network for unsupervised learning. It showed relatively consistent performance across different datasets, unlike such classification models. In addition, the unsupervised learning model provides drug-likeness scores that well separate distributions with increasing mean values in the order of datasets composed of molecules at a later step in a drug development process, whereas the classification model predicted a polarized distribution with two extreme values for all datasets presumably due to the overconfident prediction for unseen data. Thus, this new concept offers a pragmatic tool for drug-likeness scoring and further can be applied to other biochemical applications.
Identifiers
What OpenQuestion holds
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.