Abstract:
Chemsules 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.
