Artificial Intelligence & Computational Biology Validation
Bridging the Gap Between AI Models & Experimental Validation
While AI-driven drug discovery generates structural predictions at an unprecedented scale, these models often fail to accurately predict binding sites, target response, allosteric regulation, and protein flexibility—necessitating real-world validation. Traditional structural biology methods, such as cryo-EM and X-ray crystallography, require months or even years to confirm AI-generated hypotheses, creating a bottleneck in drug development.
Only the Fox Footprinting platform is capable of rapidly adjudicating and informing AI-generated drug discovery models in just days, solving a critical gap in computational biology. Fox Systems enable high-resolution, real-time validation of protein-drug interactions by mapping solvent accessibility and conformational changes, reducing workflow complexity and costs while ensuring scalable validation of AI models.
In a recent study, Fox Technology confirmed AI-predicted drug binding and target response within a single week, while traditional methods required 6 to 8 months for validation. Subsequent results from cryo-EM and X-ray crystallography ultimately confirmed Fox’s rapid footprinting data, proving its reliability and speed.
By integrating high-speed, empirical validation into AI-driven workflows, Fox Footprinting accelerates the drug discovery process, reduces costs, and actualizes the true potential of computational biology—delivering actionable results within days.