{"doi":"10.1111/bph.17330","title":"Network medicine and systems pharmacology approaches to predicting adverse drug effects","abstract":"<jats:sec>\n                    <jats:label/>\n                    <jats:p>Identifying and understanding the relationships between drug intake and adverse effects that can occur due to inadvertent molecular interactions between drugs and targets is a difficult task, especially considering the numerous variables that can influence the onset of such events. The ability to predict these side effects in advance would help physicians develop strategies to avoid or counteract them. In this article, we review the main computational methods for predicting side effects caused by drug molecules, highlighting their performance, limitations and application cases. Furthermore, we provide an overall view of resources, such as databases and tools, useful for building side effect prediction analyses.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>LINKED ARTICLES</jats:title>\n                    <jats:p>\n                      This article is part of a themed issue Network Medicine and Systems Pharmacology. To view the other articles in this section visit\n                      <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"http://onlinelibrary.wiley.com/doi/10.1111/bph.v183.8/issuetoc\">http://onlinelibrary.wiley.com/doi/10.1111/bph.v183.8/issuetoc</jats:ext-link>\n                    </jats:p>\n                  </jats:sec>","journal":"British Journal of Pharmacology","year":2026,"id":631308,"datarank":0.3453877639491069,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.0,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"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":242688,"name":"Giulia Fiscon","orcid":"0000-0002-3354-8203","position":1,"is_corresponding":false},{"id":242692,"name":"Paola Paci","orcid":"0000-0002-9393-2047","position":2,"is_corresponding":false},{"id":1635991,"name":"Alessio Funari","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Network medicine and systems pharmacology approaches to predicting adverse drug effects","abstract":"<jats:sec>\n                    <jats:label/>\n                    <jats:p>Identifying and understanding the relationships between drug intake and adverse effects that can occur due to inadvertent molecular interactions between drugs and targets is a difficult task, especially considering the numerous variables that can influence the onset of such events. The ability to predict these side effects in advance would help physicians develop strategies to avoid or counteract them. In this article, we review the main computational methods for predicting side effects caused by drug molecules, highlighting their performance, limitations and application cases. Furthermore, we provide an overall view of resources, such as databases and tools, useful for building side effect prediction analyses.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>LINKED ARTICLES</jats:title>\n                    <jats:p>\n                      This article is part of a themed issue Network Medicine and Systems Pharmacology. To view the other articles in this section visit\n                      <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"http://onlinelibrary.wiley.com/doi/10.1111/bph.v183.8/issuetoc\">http://onlinelibrary.wiley.com/doi/10.1111/bph.v183.8/issuetoc</jats:ext-link>\n                    </jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":2.302585092994046,"endowment":2.302585092994046,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39262113","pmcid":null,"openalex_id":"https://openalex.org/W4402497081","authors":[],"funders":[{"funder_name":"Sapienza Università di Roma","grant_id":"RM12117A34663A2C","title":null},{"funder_name":"Sapienza Università di Roma","grant_id":"RP12218139131053","title":null},{"funder_name":"Finalizzata Giovani Ricercatori 2021","grant_id":"GR-2021-12372614(CUP","title":null},{"funder_name":"Finalizzata Giovani Ricercatori 2021","grant_id":"B83C22007560001)","title":null}],"total_grants":4,"fwci":2.2738,"citation_percentile":0.89480016,"influential_citations":0,"citation_trend":[{"year":2024,"count":1},{"year":2025,"count":5},{"year":2026,"count":3}],"oa_status":"hybrid","license":"cc-by-nc-nd","oa_locations":[{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/bph.17330","host_type":"journal"},{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/bph.17330","host_type":"HYBRID"},{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/bph.17330","host_type":"publisher"},{"url":"https://bpspubs.onlinelibrary.wiley.com/doi/pdf/10.1111/bph.17330","host_type":"publisher"},{"url":"https://bpspubs.onlinelibrary.wiley.com/doi/full-xml/10.1111/bph.17330","host_type":"publisher"},{"url":"https://doi.org/10.1111/bph.17330","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/39262113","host_type":"repository"},{"url":"https://hdl.handle.net/11573/1719907","host_type":"repository"}],"fields_of_study":["Computational Drug Discovery Methods","Pharmacogenetics and Drug Metabolism","Cholinesterase and Neurodegenerative Diseases","Medicine","Computer Science","Humans","Drug-Related Side Effects and Adverse Reactions","Network Pharmacology","Animals"],"mesh_terms":["Network Pharmacology","Animals","Humans","Drug-Related Side Effects and Adverse Reactions"],"keywords":["Drug","Adverse effect","Task (project management)","Computer science","Risk analysis (engineering)","Drug discovery","Medicine","Side effect (computer science)","Systems pharmacology","Intensive care medicine","Pharmacology","Data science","Bioinformatics","Biology","Engineering","data mining","Network Medicine","Drug Side‐effects Estimation"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-05T23:17:51.899529Z","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":[]}