ReviewiScience2021
Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models.
Review in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 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
59 citing papers in PubMed.
- DeepFusion-ACSM: an interpretable hybrid ML-DL ensemble model for anticancer small-molecule activity prediction.Journal of computer-aided molecular design · 2026Article
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Interpretable QSAR modelling for PPAR-γ agonist prediction by integrating a stacking strategy, docking, and MD simulations.Molecular diversity · 2026Article
- Central toxic effects of an antitumoral bufadienolide: In silico targets and in vivo findings.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Machine learning-enhanced nano-QSAR and multiscale modeling for predictive nanomedicine: applications in herbal therapeutics and neglected tropical diseases.Discover nano · 2026Review
- The (r)evolution of chemical space and molecular modeling: a time-resolved perspective.Journal of computer-aided molecular design · 2026Review
- In Silico Regression Modeling and Improved Interpretability To Predict the Transport Inhibitory Activity of Breast Cancer Resistance Protein.ACS omega · 2026Article
- Structure-based virtual screening identifies VX-809 as a candidate dual-pathway modulator in fuchs endothelial corneal dystrophy.Scientific reports · 2026Article
- XGBPred-ACSM: A Hybrid Descriptor-Driven XGBoost Framework for Anticancer Small Molecule Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- AI-driven generative framework integrating ML-QSAR and fragment learning for isoform-selective PI3K inhibitor design.Molecular diversity · 2026Article
- Nanoparticle approaches for hepatitis therapy and clinical translation.Discover nano · 2026Review
- Computational Study of Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Tuberculosis: An Ongoing Global Threat.Advances in experimental medicine and biology · 2026Review
- Artificial Intelligence Driven Innovation: Advancing Mesenchymal Stem Cell Therapies and Intelligent Biomaterials for Regenerative Medicine.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence-Driven Nanoarchitectonics for Smart Targeted Drug Delivery.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Optimizing Multi-Task Learning with Evolutionary Relatedness Metrics for Enhanced QSAR-Based Natural Product Activity Prediction.ACS omega · 2025Article
- Titania: an integrated tool for in silico molecular property prediction and NAM-based modeling.Molecular diversity · 2025Article
- Machine learning, network pharmacology, and molecular dynamics reveal potent cyclopeptide inhibitors against dengue virus proteins.Molecular diversity · 2025Article
- Machine learning models to identify lead compound and substitution optimization to have derived energetics and conformational stability through docking and MD simulations for sphingosine kinase 1.Molecular diversity · 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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models. We first review the history of traditional QSAR and point out its problems. We then propose a better QSAR model characterized by a new iterative framework to integrate machine learning with disparate data input. Finally, we discuss the application of QSAR and machine learning to many practical research fields, including drug development and clinical trials.
Indexed as
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.