ArticleJournal of medicinal chemistry2014
QSAR modeling: where have you been? Where are you going to?
Article in Journal of medicinal chemistry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 619 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
619 citing papers in PubMed.
- Nucleo-amino acid derived AMO as a potential JAK inhibitor: machine learning screening, docking, molecular dynamics, ADMET/toxicity, and MM-PBSA analysis.Current research in structural biology · 2026Article
- Musical Peptide: Fragmentation of Musical Protein for AI-driven Bioactive Peptide Discovery Based on a New DeepPepQSAR Pipeline.Interdisciplinary sciences, computational life sciences · 2026Article
- TechBio 3.0 closes the drug discovery loop with multimodal AI and generative chemistry.npj drug discovery · 2026Review
- Discovery of PIM-1 kinase inhibitors from marine natural products through machine learning and structure-based screening.Research in pharmaceutical sciences · 2026Article
- A molecular modeling and machine learning-based approach for the identification of potential human ALOX15 inhibitors.Journal of molecular modeling · 2026Article
- Physical priors improve performance of structure-based binding affinity models.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence in toxicology: current advances, challenges and future directions.Archives of toxicology · 2026Review
- 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
- Structure-Based Virtual Screening andACS omega · 2026Article
- Is AI Capable of Real-World Drug Discovery?Journal of chemical information and modeling · 2026Article
- Multi-Endpoint Prediction of Bioactivity and ADMET Properties Coupled with Multi-Objective Optimization in Molecular Descriptor Space for Candidate ERα Antagonists.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Development of an Interpretable QSAR Model for Predicting Coagulation Factor XIIa Inhibitors Using Ensemble Machine Learning.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Machine learning-driven identification of PIM2 kinase inhibitors through QSAR modeling and molecular dynamics simulations.Journal, genetic engineering & biotechnology · 2026Article
- Quantitative Structure-Activity Relationship Approaches for Exploring the Anticancer Potential of Flavonoids.Chemistry & biodiversity · 2026Review
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- Discovery of novel acridine based inhibitors of Bruton's tyrosine kinase (BTK) via pharmacophore modeling and machine learning-driven virtual screening followed by molecular dynamic studies.Journal of computer-aided molecular design · 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
- Benchmarking Classical and Quantum Machine Learning for Potency Prediction across Ten Therapeutically Relevant Targets.ACS medicinal chemistry letters · 2026Article
- Advances in Machine Learning-Enhanced PBPK Models for Brain-Targeted Drug Delivery via Nanocarriers: A Comprehensive Review.Journal of functional biomaterials · 2026Review
559 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
Abstract
Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.
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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.