Projects

OncoHelper AI Interceptor is in development as an AI system for pediatric adaptive oncology. It treats the tumor as a moving target and uses time as a core variable, updating forecasts as new clinical data arrives.The project is an internal, investigator-initiated effort developed by OncoHelper AI. Its architecture is defined, and a two-year research plan is already in place.The research draws on established methods from target tracking, sequential decision-making, neural dynamical modeling, and other time-series forecasting approaches. Donations support implementation, testing and validation, clinician-facing workflow design, and privacy-ready data preparation for measurable, reportable outcomes.

This project is in development to create and validate chickpea-derived peptide–mineral complexes for supportive care, starting with a bioavailable zinc peptide supplement and expanding to an iron-binding peptide track from the same platform. The work focuses on rigorous analytical characterization, absorption and transport testing, functional bioactivity assays, and a scalable production pathway, with milestone-based progress reporting.

A focused effort to map and influence the earliest measurable shifts in protein behavior, pairing fast laboratory readouts with an iterative intervention cycle so we can move from observation to controlled, reproducible change. This work is designed as a reusable capability that can later support pediatric oncology research by helping study how early protein misfolding and aggregation may affect treatment response.

Many children in high risk regions live with hereditary anemias that could be cured, but in practice receive only lifelong transfusions. This project is in development to build pediatric clinical and genetic cohorts that identify children with hereditary anemias who could realistically move from lifelong transfusions to curative options such as risk adapted bone marrow transplantation, with a future pathway to gene therapy. The work is being developed with established research and clinical partners in several countries, coordinated by OncoHelper AI. Donations fund cohort building, genetics and clinical characterization, pathway design, and measurable outcomes reporting.

Every child responds to cancer therapy differently. Yet today, most treatment decisions still follow standardized pathways that cannot fully capture individual risks. This project is in development as an AI-supported clinical decision approach that helps pediatric oncologists tailor therapy intensity to each child over time, reducing toxicity while protecting cure rates. It is being developed with partner institutes and clinical teams in multiple countries, coordinated by OncoHelper AI, to ensure real clinic relevance. Donations fund cohort readiness, model development, validation, and clinician-oriented implementation milestones.

This project is in development to advance next generation PARP1 inhibitor candidates based on olaparib thiodiazole hybrid chemistry, with the goal of addressing treatment resistance and expanding response populations. The program is being developed in collaboration with experienced chemistry and laboratory teams across several countries, including Central Asia. Donations support computational work, synthesis, characterization, in vitro testing, and disciplined milestone tracking with transparent reporting.