Publication: Machine learning-accelerated discovery of metal–organic frameworks for storage and delivery of anticancer drug molecules
| dc.contributor.department | Department of Chemical and Biological Engineering | |
| dc.contributor.kuauthor | Gülbalkan, Hasan Can | |
| dc.contributor.kuauthor | Keskin, Seda | |
| dc.contributor.schoolcollegeinstitute | College of Engineering | |
| dc.date.accessioned | 2026-09-15T10:56:50Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | The integrated DFT, molecular simulation, and ML framework screens 90 653 MOFs for 5-FU and MTX storage and delivery. Top candidates with promising drug adsorption capacities were identified and drug diffusion within their pores was examined. | |
| dc.description.harvestedfrom | Manual | |
| dc.description.indexedby | Scopus | |
| dc.description.indexedby | WOS | |
| dc.description.publisherscope | International | |
| dc.description.readpublish | RSC RP | |
| dc.description.sponsoredbyTubitakEu | EU | |
| dc.description.sponsorship | HORIZON EUROPE European Research Council (Grant: ERC Consolidator Grant 101124002) | |
| dc.description.version | Published Version | |
| dc.identifier.ScopusPercentile | 72 | |
| dc.identifier.ScopusQuartile | Q2 | |
| dc.identifier.WoSPercentile | 83.5 | |
| dc.identifier.WoSQuartile | Q1 | |
| dc.identifier.doi | 10.1039/d6dd00223d | |
| dc.identifier.endpage | 3434 | |
| dc.identifier.grantno | ERC Consolidator Grant 101124002 | |
| dc.identifier.issn | 2635-098X | |
| dc.identifier.issue | 8 | |
| dc.identifier.scopus | 2-s2.0-105046025423 | |
| dc.identifier.startpage | 3420 | |
| dc.identifier.uri | http://doi.org/10.1039/d6dd00223d | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14288/35521 | |
| dc.identifier.volume | 5 | |
| dc.identifier.wos | 001827137000001 | |
| dc.keywords | Anticancer drug | |
| dc.keywords | Drug | |
| dc.keywords | Drug delivery | |
| dc.keywords | Drug discovery | |
| dc.keywords | Small molecule | |
| dc.keywords | Cancer | |
| dc.language | eng | |
| dc.publisher | Royal Society of Chemistry (RSC) | |
| dc.relation.affiliation | Koç University | |
| dc.relation.collection | Koç University Institutional Repository | |
| dc.relation.ispartof | Digital Discovery | |
| dc.relation.openaccess | N/A | |
| dc.subject | Physical sciences | |
| dc.subject | Chemistry | |
| dc.subject | Inorganic chemistry | |
| dc.subject | Materials science | |
| dc.subject | Materials chemistry | |
| dc.title | Machine learning-accelerated discovery of metal–organic frameworks for storage and delivery of anticancer drug molecules | |
| dc.type | Journal Article | |
| dspace.entity.type | Publication | |
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