Please use this identifier to cite or link to this item: http://repository.iiitd.edu.in/xmlui/handle/123456789/1617
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dc.contributor.advisorAhuja, Gaurav (Advisor)
dc.contributor.authorPankajbhai, Rathod Kunal
dc.contributor.authorTyagi, Sarthak
dc.date.accessioned2024-05-27T05:47:11Z
dc.date.available2024-05-27T05:47:11Z
dc.date.issued2023-11-29
dc.identifier.urihttp://repository.iiitd.edu.in/xmlui/handle/123456789/1617
dc.description.abstractChemsules introduces a revolutionary approach to carcinogenicity assessment, anchored by the precision-driven Metabokiller machine learning model. Catering to a diverse user base, including researchers, scientists, industry professionals, students, and enthusiasts, the platform offers a seamless web interface for compound analysis. Metabokiller's comprehensive evaluation covers electrophilicity, proliferation induction, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response, providing a holistic understanding of compound carcinogenicity. The website features intuitive input methods, including SMILES string entry and graphical structure depiction. Ensuring data integrity and user confidentiality, the platform's scalability is showcased through an API for further development. This user-centric approach aims to democratize access to predictive insights, fostering advancements in compound carcinogenicity understanding.en_US
dc.language.isoen_USen_US
dc.publisherIIIT-Delhien_US
dc.subjectCarcinogenicity predictionen_US
dc.subjectMetabokilleren_US
dc.subjectmachine learningen_US
dc.subjectpredictive methodologiesen_US
dc.subjectSMILESen_US
dc.subjectcompound analysisen_US
dc.subjectuser-centric interfaceen_US
dc.subjectAPI integrationen_US
dc.titleDevelopment of chemsulesen_US
dc.typeOtheren_US
Appears in Collections:Year-2023

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