ArticleMethods in molecular biology (Clifton, N.J.)2022
Machine Learning for In Silico ADMET Prediction.
Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Multifaceted Integrated Analysis of CDK1 and TOP2A Signaling Pathways for Multi-Target Therapeutic Intervention in Epithelial Ovarian Cancer.International journal of molecular sciences · 2026Article
- Uncertainty Quantification in Molecular Machine Learning for Property Predictions under Data Shifts.Journal of chemical information and modeling · 2026Article
- Integrating Synthesis, Pharmacological Activity, and Molecular Modeling to Explore 1,2,4-Triazine Fused Thiazolidin-4-one Derivatives as GABACNS & neurological disorders drug targets · 2026Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- MolGraph-xLSTM as a graph-based dual-level xLSTM framework for enhanced molecular representation and interpretability.Communications chemistry · 2025Article
- Human Brain Penetration Prediction Using Scaling Approach from Animal Machine Learning Models.The AAPS journal · 2023Article
- Analysis of Functional Promoter of Camel FGF21 Gene and Identification of Small Compounds Targeting FGF21 Protein.Veterinary sciences · 2023Article
- From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output.Frontiers in pharmacology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
ADMET (absorption, distribution, metabolism, excretion, and toxicity) describes a drug molecule's pharmacokinetics and pharmacodynamics properties. ADMET profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safety are considered some of the major causes of clinical attrition in the development of new chemical entities. In past decades, various machine learning or quantitative structure-activity relationship (QSAR) methods have been successfully integrated in the modeling of ADMET. Recent advances have been made in the collection of data and the development of various in silico methods to assess and predict ADMET of bioactive compounds in the early stages of drug discovery and development process.
Indexed as
Identifiers
34731482What 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.