Integrative Personalized Oncology: Leveraging Multi-Source Data and Drug Solubility for Optimal Cancer Treatment

The rise of personalized medicine in oncology has highlighted the need for tailored treatment strategies, as traditional approaches often overlook the unique genetic and molecular profiles of cancer patients. Approximately 30-40 percentage of patients experience inadequate responses to standard therapies, underscoring the critical importance of understanding tumor heterogeneity. Despite advancements in cancer research, there remains a gap in effectively integrating diverse patient data—such as genomic, proteomic, lifestyle, and environmental factors—into actionable treatment plans. This project aims to develop a comprehensive, machine learning-driven framework for personalized oncology that leverages multi-source data to optimize treatment recommendations. Employing ensemble modeling techniques, including random forests and deep neural networks, the framework enhances predictive accuracy while addressing challenges related to drug solubility and bioavailability through cheminformatics. By continuously monitoring patient outcomes and refining predictive models, the research seeks to provide dynamic, evidence-based treatment plans tailored to individual needs. Ultimately, the key message of this study is that integrating advanced computational methods with diverse data sources can significantly improve the efficacy of cancer treatments and patient outcomes, paving the way for a more personalized approach in oncology.