CertisAI is engineered to deliver high-confidence predictions by learning from diverse, high-fidelity training data that integrates both computational features and validated in vivo outcomes; however, it cannot—and should not—be expected to achieve perfect accuracy.
Predictive models, by their nature, identify probabilistic patterns across molecular structures and biological responses, enabling generalization to novel compounds beyond those explicitly seen during training. However, this very generalization introduces inherent limitations: compounds with structural motifs or mechanisms underrepresented in the platform’s training set may yield false positives or false negatives, as the model lacks sufficient precedent to capture their unique behavior.
We have implemented rigorous safeguards against overfitting and prioritized translational relevance, but biological complexity—shaped by factors like tumor heterogeneity, pharmacokinetics, and context-dependent resistance mechanisms—means that no computational model can fully replicate the nuance of empirical validation. Our goal is not to replace experimental testing, but to provide a robust, data-driven starting point that accelerates therapeutic prioritization and refines the path to validation.