{"doi":"10.3390/ijms26041617","title":"Exploring Potential Medications for Alzheimer’s Disease with Psychosis by Integrating Drug Target Information into Deep Learning Models: A Data-Driven Approach","abstract":"Approximately 50% of Alzheimer's disease (AD) patients develop psychotic symptoms, leading to a subtype known as psychosis in AD (AD + P), which is associated with accelerated cognitive decline compared to AD without psychosis. Currently, no FDA-approved medication specifically addresses AD + P. This study aims to improve psychosis predictions and identify potential therapeutic agents using the DeepBiomarker deep learning model by incorporating drug-target interactions. Electronic health records from the University of Pittsburgh Medical Center were analyzed to predict psychosis within three months of AD diagnosis. AD + P patients were classified as those with either a formal psychosis diagnosis or antipsychotic prescriptions post-AD diagnosis. Two approaches were employed as follows: (1) a drug-focused method using individual medications and (2) a target-focused method pooling medications by shared targets. The updated DeepBiomarker model achieved an area under the receiver operating curve (AUROC) above 0.90 for psychosis prediction. A drug-focused analysis identified gabapentin, amlodipine, levothyroxine, and others as potentially beneficial. A target-focused analysis highlighted significant proteins, including integrins, calcium channels, and tyrosine hydroxylase, confirming several medications linked to these targets. Integrating drug-target information into predictive models improves the identification of medications for AD + P risk reduction, offering a promising strategy for therapeutic development.","journal":"International Journal of Molecular Sciences","year":2025,"id":550178,"datarank":0.1773785833733061,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.012586740073089643,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.012586740073089643,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":2,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9593,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":593416,"name":"Chen Jiang","orcid":"0000-0001-8336-6814","position":1,"is_corresponding":false},{"id":332280,"name":"Xiguang Qi","orcid":"0000-0003-0325-2118","position":2,"is_corresponding":false},{"id":98,"name":"Julia Kofler","orcid":"0000-0003-4298-7328","position":3,"is_corresponding":false},{"id":71006,"name":"Robert A. Sweet","orcid":"0000-0001-9154-9709","position":4,"is_corresponding":false},{"id":992351,"name":"Lirong Wang","orcid":"0000-0001-9172-3746","position":5,"is_corresponding":false},{"id":472526,"name":"Oshin Miranda","orcid":null,"position":0,"is_corresponding":true}],"reference_count":81,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:54:16.596730Z","pmid":"40004081","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":[]}