Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 4 complete abstracts (570 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
The relationship between the chemical structure of a compound and its biological or pharmacological activity. Compounds are often classed together because they have structural characteristics in common including shape, size, stereochemical arrangement, and distribution of functional groups.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Relationships, Structure-Activity; Relationship, Structure-Activity; Structure Activity Relationship; Structure-Activity Relationships.
Where it fits in the MeSH classification
- Broader topics
- Biochemical Phenomena; Pharmacological Phenomena
- Narrower topics
- Quantitative Structure-Activity Relationship
Research landscape
PubMed indexes 227,834 records tagged with the MeSH heading “Structure-Activity Relationship” (counts retrieved 2026-10-29). 39,589 of them (17.4%) were published from 2020 onward, and the busiest period so far is through 1999 with 75,410 records.
Reviews account for 23,423 records (10.3%) and meta-analyses for 33. Randomized controlled trials number 107 (0.0%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 75,410 |
| 2000-2009 | 50,408 |
| 2010-2019 | 62,427 |
| 2020 onward | 39,589 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 23,423 | 10.3% |
| Meta-analyses | 33 | 0.0% |
| Randomized controlled trials | 107 | 0.0% |
Method: PubMed query "Structure-Activity Relationship"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in pharmacology
Drug evidence builds in layers: laboratory pharmacology describes how a compound acts, pharmacokinetic studies describe how the body handles it, and randomized trials test whether it helps and what harms it causes. Post-marketing surveillance and observational studies reveal rarer adverse effects but cannot prove causation. This page is educational and is not a recommendation to start, stop or change any medicine; decisions about treatment belong with a qualified clinician.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| A Structure-Based Drug Discovery Paradigm | 2019 | Review article Design not recorded in structured metadata | 387 | cc by PMID 31174387 · DOI · PMC6601033 |
| Comprehensive ensemble in QSAR prediction for drug discovery | 2019 | Research article Design not recorded in structured metadata | 104 | cc by PMID 31655545 · DOI · PMC6815455 |
| Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites | 2021 | Research article Design not recorded in structured metadata | 80 | cc by PMID 34099674 · DOI · PMC8185074 |
| Defining the Data set Defines the QSAR Claim | 2026 | Research article Design not recorded in structured metadata | 2 | cc by PMID 41755499 · DOI · PMC13014448 |
A Structure-Based Drug Discovery Paradigm [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method.
- Closing excerpt
- In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (133 words)
Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the "big data" generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 31174387 · DOI · PMC6601033
Comprehensive ensemble in QSAR prediction for drug discovery [2]
Abstract reading summary — source excerpts
- Opening excerpt
- BackgroundQuantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities.
- Closing excerpt
- The proposed individual models did not show impressive results as a single model, but it was considered the most important predictor when combined, according to the interpretation of the meta-learning.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (185 words)
BackgroundQuantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions. Ensemble learning builds a set of diversified models and combines them. However, the most prevalent approach random forest and other ensemble approaches in QSAR prediction limit their model diversity to a single subject.ResultsThe proposed ensemble method consistently outperformed thirteen individual models on 19 bioassay datasets and demonstrated superiority over other ensemble approaches that are limited to a single subject. The comprehensive ensemble method is publicly available at http://data.snu.ac.kr/QSAR/ .ConclusionsWe propose a comprehensive ensemble method that builds multi-subject diversified models and combines them through second-level meta-learning. In addition, we propose an end-to-end neural network-based individual classifier that can automatically extract sequential features from a simplified molecular-input line-entry system (SMILES). The proposed individual models did not show impressive results as a single model, but it was considered the most important predictor when combined, according to the interpretation of the meta-learning.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 31655545 · DOI · PMC6815455
Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult.
- Closing excerpt
- These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (136 words)
Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult. To address this challenge, we develop a computational "anchor extension" methodology for targeting protein interfaces by extending a peptide chain around a non-canonical amino acid residue anchor. To test our approach using a well characterized model system, we design cyclic peptides that inhibit histone deacetylases 2 and 6 (HDAC2 and HDAC6) with enhanced potency compared to the original anchor (IC50 values of 9.1 and 4.4 nM for the best binders compared to 5.4 and 0.6 µM for the anchor, respectively). The HDAC6 inhibitor is among the most potent reported so far. These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 34099674 · DOI · PMC8185074
Defining the Data set Defines the QSAR Claim [4]
Abstract reading summary — source excerpts
- Opening excerpt
- Machine learning has greatly expanded QSAR modeling, but predictive claims still depend on choices that are rarely documented: how chemicals are represented, how end points are defined, and how evaluations are designed.
- Closing excerpt
- These contracts are feasible with current open-source tools and would shift the field from architecture-centric comparisons toward claims that are transparent, reproducible, and trustworthy.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (116 words)
Machine learning has greatly expanded QSAR modeling, but predictive claims still depend on choices that are rarely documented: how chemicals are represented, how end points are defined, and how evaluations are designed. In the era of benchmarks and foundation models, inconsistent standardization, unclear rules for combining measurements, and hidden information leakage routinely inflate reported performance while obscuring weaknesses that matter for real-world applications. We propose data set contracts: executable, auditable documents that explicitly declare chemical processing rules, end point definitions, aggregation logic, data splits, and leakage diagnostics for the intended prediction scenario. These contracts are feasible with current open-source tools and would shift the field from architecture-centric comparisons toward claims that are transparent, reproducible, and trustworthy.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41755499 · DOI · PMC13014448
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Mass spectrometry-based proteomics turns quantitative [5]
- Research article Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017-Utility and Limitations [6]
- Research article 3D-QSAR in drug design--a review [7]
- Research article Antibacterial activity of flavonoids and their structure-activity relationship: An update review [8]
- Research article Structure and physicochemical properties of octenyl succinic anhydride modified starches: a review [9]
- Research article GH11 xylanases: Structure/function/properties relationships and applications [10]
References
- Batool M, Ahmad B, Choi S. A Structure-Based Drug Discovery Paradigm. . 2019. PMID 31174387 · DOI · PMC6601033
- Kwon S, Bae H, Jo J, Yoon S. Comprehensive ensemble in QSAR prediction for drug discovery. . 2019. PMID 31655545 · DOI · PMC6815455
- Hosseinzadeh P, Watson PR, Craven TW, Li X, Rettie S, Pardo-Avila F, et al.. Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites. . 2021. PMID 34099674 · DOI · PMC8185074
- Nael MA, Alakonda LM, Elokely KM. Defining the Data set Defines the QSAR Claim. . 2026. PMID 41755499 · DOI · PMC13014448
- Ong SE, Mann M. Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol. 2005. PMID 16408053 · DOI
- Baell JB, Nissink JWM. Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017-Utility and Limitations. ACS Chem Biol. 2018. PMID 29202222 · DOI · PMC5778390
- Verma J, Khedkar VM, Coutinho EC. 3D-QSAR in drug design--a review. Curr Top Med Chem. 2010. PMID 19929826 · DOI
- Farhadi F, Khameneh B, Iranshahi M, Iranshahy M. Antibacterial activity of flavonoids and their structure-activity relationship: An update review. Phytother Res. 2019. PMID 30346068 · DOI
- Sweedman MC, Tizzotti MJ, Schäfer C, Gilbert RG. Structure and physicochemical properties of octenyl succinic anhydride modified starches: a review. Carbohydr Polym. 2013. PMID 23218383 · DOI
- Paës G, Berrin JG, Beaugrand J. GH11 xylanases: Structure/function/properties relationships and applications. Biotechnol Adv. 2012. PMID 22067746 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
See how EvidenceLib is built. Spotted an error? Email support@evidencehelix.com.
Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.