Molecule Toxicity Analysis Trees for Fragment-Level Attribution

The patent performs preliminary toxicity assessments using machine learning models before physical synthesis and cell culture testing. The system generates toxicity predictions and dose-response curves computationally, allowing researchers to prioritize which molecules warrant expensive and time-consuming wet lab experiments, thereby accelerating the overall drug development process while maintaining comprehensive toxicity characterization for selected candidates

The patent creates computational copies of physical toxicity testing through machine learning models trained on existing toxicity data. These digital twins simulate toxicity outcomes without requiring actual cell culture experiments, enabling rapid screening of multiple molecules. The model predictions serve as proxies for physical testing, reducing resource consumption while identifying molecules that require follow-up experimental validation

The patent employs a multi-functional machine learning framework that simultaneously predicts multiple toxicity dimensions (cell morphology changes, membrane integrity, metabolic activity, apoptosis, necrosis, cell count, and percent cell viability) using a single integrated model system. This universal approach allows comprehensive toxicity characterization across all major dimensions without requiring separate experiments for each parameter, thereby maintaining measurement precision while accelerating productivity

The system incorporates feedback loops where machine learning predictions are continuously refined based on actual cell culture test results. The model learns from experimental data to improve its predictions, and the iterative process ensures that computational predictions become increasingly accurate over time. This feedback mechanism bridges the gap between rapid ML screening and comprehensive experimental validation

The patent uses machine learning models to perform preliminary predictions of toxicity at multiple dose values before conducting physical experiments. The model generates predicted dose-response curves that identify the most informative dose ranges and critical response points. This preliminary computational analysis guides the design of actual experiments, allowing researchers to focus resources on measuring the most critical dose points while maintaining accurate dose-response characterization

The system performs computational toxicity predictions at a comprehensive set of dose values (excessive action) to generate detailed dose-response curves, then uses these predictions to identify the subset of critical dose points that require actual experimental measurement (partial action). This approach maintains measurement precision by ensuring all critical regions of the dose-response relationship are captured, while reducing the total number of physical experiments required