ReviewChemical Society reviews2020
QSAR without borders.
Review in Chemical Society reviews, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 275 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
275 citing papers in PubMed.
- Musical Peptide: Fragmentation of Musical Protein for AI-driven Bioactive Peptide Discovery Based on a New DeepPepQSAR Pipeline.Interdisciplinary sciences, computational life sciences · 2026Article
- Toward FOXM1 inhibitors based on supervised machine learning classification models and similarity-based screening.Journal of computer-aided molecular design · 2026Article
- Interpretable Machine Learning Frameworks for QSAR Modeling: A Comparative Study of Classical Algorithms and Transformer-CNN Architectures.ACS omega · 2026Article
- Physical priors improve performance of structure-based binding affinity models.bioRxiv : the preprint server for biology · 2026Article
- 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
- A novel framework for the discovery of MAPK-activated protein kinase 2 (MAPKAPK2) inhibitors using a multi-feature deep learning ensemble.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
- Multi-objective optimization in the context of generative chemistry.Nature communications · 2026Review
- Machine learning-driven identification of PIM2 kinase inhibitors through QSAR modeling and molecular dynamics simulations.Journal, genetic engineering & biotechnology · 2026Article
- Mixture-Quantitative Structure-Property Relationship Approaches for Jet Fuel-Component Blends: A Review.Molecular informatics · 2026Review
- AI-driven drug design: a comprehensive review.Journal of computer-aided molecular design · 2026Review
- Integrated AI-Driven Discovery of MAPK3 Inhibitors for Oral Inflammatory and Proliferative Diseases.Pharmaceuticals (Basel, Switzerland) · 2026Article
- In-silico toxicity study of tryptamine, psilocin, psilocybin, N,N-dimethyltryptamine, 5'-methoxy-N,N-dimethyltryptamine and O-acetylpsilocin.Archives of toxicology · 2026Article
- Precision engineering of anti-atherosclerotic herbal nanomedicine: from machine learning-aided active components screening to optimized metal-phenolic network codelivery.Drug delivery and translational research · 2026Article
- Activity-cliff awareness enables robust graph learning for molecular property prediction.Nature communications · 2026Article
- NMR-AI: An Open Platform for NMR-Enhanced Molecular Representations and Physicochemical Property Prediction.Journal of chemical information and modeling · 2026Article
- DeepPepQSAR: all-in-one for comprehensively exploiting the vast molecular diversity space of bioactive peptide universe.Molecular diversity · 2026Article
- A Decision-Oriented Calibrated Screening Workflow for Tylosin Derivatives: Closed-Loop MIC Validation AgainstAntibiotics (Basel, Switzerland) · 2026Article
- Data-Driven Structure-Property Analysis and Rational-Efficient Retrosynthetic Design of 1,2-Dihydronaphtho[2,1‑ACS omega · 2026Article
- De novo design and experimental characterization of bitter peptides.NPJ science of food · 2026Article
215 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issuedCorrection: QSAR without borders.2020 · Curtalolo, Stefano [corrected to Curtarolo, Stefano]
Authors and funding
19 authors.
Funding
Abstract
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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.