Bioisosteric Fragment Replacement for Lower-Toxicity Molecules

The patent applies preliminary action by using machine learning models to predict toxicity outcomes before physical synthesis and testing are performed. The system processes molecular structures through trained ML models to generate toxicity predictions, enabling early filtering of problematic compounds and reducing the number of physical experiments needed, thus saving time while maintaining comprehensive toxicity characterization.

The patent applies copying by creating virtual representations of molecular toxicity through machine learning models instead of performing physical synthesis and testing. The ML models are trained on existing toxicity data and then used to generate predictions for new molecules, effectively copying the information-gathering process in silico rather than in vitro, thereby reducing resource consumption while maintaining data accuracy.

The patent applies universality by designing a multi-functional machine learning system that predicts multiple toxicity dimensions simultaneously using a single model framework. The system processes molecular structures through various ML models that output predictions for different toxicity endpoints, enabling comprehensive toxicity characterization across multiple dimensions while consuming fewer resources than separate physical assays for each endpoint.

The patent applies preliminary action by performing toxicity assessments early in the drug discovery process using machine learning predictions before committing to costly and time-consuming physical synthesis and testing. This early filtering maintains thorough toxicity assessment through comprehensive ML modeling while accelerating drug development speed by identifying problematic compounds before they advance to later stages.