ArticleACS omega2024
Evaluation and Optimization Methods for Applicability Domain Methods and Their Hyperparameters, Considering the Prediction Performance of Machine Learning Models.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Design of Thermotropic Liquid Crystal Molecules With a Wide Temperature Range for the Liquid Crystal Phase Using Machine Learning.Molecular informatics · 2026Article
- Machine-Learning-Driven Molecular Design and Structure-Property-Performance Relationships in Pharmaceutical Chemistry.Molecules (Basel, Switzerland) · 2026Review
- Comparative evaluation and selection of optimal QSAR-based machine learning model for liver toxicity prediction.Molecular diversity · 2026Article
- ProQSAR: A modular and reproducible framework for small-data QSAR modeling with fit-and-use models.Journal of cheminformatics · 2026Article
- Efficient quantum algorithm for the design of complex materials: quantum circuit learning.Scientific reports · 2026Article
- Computational design of MMP-13 inhibitors using a combined approach of machine learning, docking, and molecular dynamics.Molecular diversity · 2026Article
- Explicit Applicability Domain Calculations Can Help Determine When Uncertainty Estimates Are Less Reliable.ACS omega · 2026Article
- aMLProt: an automated machine learning library for protein applications.Bioinformatics (Oxford, England) · 2025Article
- Batch Process Design Including Initial and Operating Conditions and Online Property Estimation in Acrylic Resin Polymerization.ACS omega · 2025Article
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Development and experimental validation of a machine learning model for the prediction of new antimalarials.BMC chemistry · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In molecular, material, and process design and control, the applicability domain (AD) of a mathematical model
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.