Predictive computational methods for drug discovery have typically relied on models that incorporate three-dimensional information about protein structure. But these modeling methods face limitations due to high computational costs, expensive training data, and inability to fully capture protein dynamics.

Ainnocence develops predictive AI models based on target protein sequence. By bypassing 3D structural information entirely, sequence-based AI models can screen billions of drug candidates in hours or days. Ainnocence uses amino acid sequence data from target proteins and wet lab data to predict drug binding and other biological effects. They have demonstrated success in discovering COVID-19 antibodies and their platform can be used to discover other biomolecules, small molecules, cell therapies, and mRNA vaccines.