{"doi":"10.1002/minf.201700076","title":"Structure Modification toward Applicability Domain of a QSAR/QSPR Model Considering Activity/Property","abstract":"<jats:title>Abstract</jats:title><jats:p>In drug and material design, the activity and property values of the designed chemical structures can be predicted by quantitative structure−activity and structure−property relationship (QSAR/QSPR) models. When a QSAR/QSPR model is applied to chemical structures, its applicability domain (AD) must be considered. The predicted activity/property values are only reliable for chemical structures inside the AD. Chemical structures outside the AD are usually neglected, as the predicted values are unreliable. The purpose of this study is to develop a methodology for obtaining novel chemical structures with the desired activity or property based on a QSAR/QSPR model by making use of the neglected structures. We propose a structure modification strategy for the AD that considers the activity and property simultaneously. The AD is defined by a one‐class support vector machine and the structure modification is guided by a partial derivative of the AD model and matched molecular pairs analysis. Three proof‐of‐concept case studies generate novel chemical structures inside the AD that exhibit preferable activity/property values according to the QSAR/QSPR model.</jats:p>","journal":"Molecular Informatics","year":2017,"id":688231,"datarank":0.38474240361923057,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"self_citation_contribution":0.38474240361923057,"citation_network_contribution":0.0,"self_endowment_contribution":0.38474240361923057,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":2,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1797939,"name":"Tomoyuki Miyao","orcid":null,"position":1,"is_corresponding":false},{"id":1797940,"name":"Kimito Funatsu","orcid":null,"position":2,"is_corresponding":false},{"id":1797938,"name":"Shoki Ochi","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Structure Modification toward Applicability Domain of a QSAR/QSPR Model Considering Activity/Property","abstract":"<jats:title>Abstract</jats:title><jats:p>In drug and material design, the activity and property values of the designed chemical structures can be predicted by quantitative structure−activity and structure−property relationship (QSAR/QSPR) models. When a QSAR/QSPR model is applied to chemical structures, its applicability domain (AD) must be considered. The predicted activity/property values are only reliable for chemical structures inside the AD. Chemical structures outside the AD are usually neglected, as the predicted values are unreliable. The purpose of this study is to develop a methodology for obtaining novel chemical structures with the desired activity or property based on a QSAR/QSPR model by making use of the neglected structures. We propose a structure modification strategy for the AD that considers the activity and property simultaneously. The AD is defined by a one‐class support vector machine and the structure modification is guided by a partial derivative of the AD model and matched molecular pairs analysis. Three proof‐of‐concept case studies generate novel chemical structures inside the AD that exhibit preferable activity/property values according to the QSAR/QSPR model.</jats:p>","is_dataset_classified":null,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"datacite_reuse_total":2,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"28815921","pmcid":null,"openalex_id":"https://openalex.org/W2746158299","authors":[],"funders":[{"funder_name":"Japan Science and Technology Agency","grant_id":"","title":null}],"total_grants":1,"fwci":1.1457,"citation_percentile":0.80463631,"influential_citations":0,"citation_trend":[{"year":2017,"count":1},{"year":2018,"count":1},{"year":2019,"count":1},{"year":2020,"count":2},{"year":2022,"count":1},{"year":2024,"count":3},{"year":2025,"count":1},{"year":2026,"count":2}],"oa_status":"closed","license":"http://onlinelibrary.wiley.com/termsAndConditions#am","oa_locations":[{"url":"https://doi.org/10.1002/minf.201700076","host_type":"HYBRID"},{"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fminf.201700076","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/minf.201700076","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/full-xml/10.1002/minf.201700076","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/am-pdf/10.1002/minf.201700076","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/28815921","host_type":"repository"}],"fields_of_study":["Computational Drug Discovery Methods","Chemistry and Chemical Engineering","Machine Learning in Materials Science","Chemistry","Medicine","Materials Science"],"mesh_terms":["Humans","Models, Molecular","Software","Molecular Structure","Receptors, Adrenergic, alpha-2","Quantitative Structure-Activity Relationship","Databases, Protein"],"keywords":["Quantitative structure–activity relationship","Applicability domain","Property (philosophy)","Chemical structure","Domain (mathematical analysis)","Biological system","Cheminformatics","Molecular descriptor","Chemistry","Computational chemistry","Mathematics","Stereochemistry","Organic chemistry","QSAR","QSPR","Structure modification","Chemoinformatics"],"sdg_mappings":[],"linked_datasets":[{"doi":"10.6084/m9.figshare.26944167.v1","title":"Additional file 1 of Evaluating the generalizability of graph neural networks for predicting collision cross section","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.26944167","title":"Additional file 1 of Evaluating the generalizability of graph neural networks for predicting collision cross section","publisher":"figshare","resource_type":"JournalArticle"}],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-19T14:13:54.884952Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}