Our discovery engine transforms real experimental data into optimized molecular candidates through a fully integrated, AI driven workflow. Each stage of the process is designed to maximize speed, accuracy, and translational relevance.

The pipeline begins with curated experimental bioactivity data and automated initial ligand filtering, ensuring that only high quality molecular inputs enter the modeling system. In parallel, ligands and protein targets undergo standardized preparation for three dimensional molecular docking, enabling accurate simulation of protein ligand interactions.

Structure based modeling is combined with ligand based machine learning. Molecular fingerprints are computed and processed by regression models trained to predict biological activity, while docking simulations estimate binding strength and interaction quality. These complementary predictions continuously inform candidate prioritization.

Selected hit structures are further refined through machine learning guided recomposition, enabling the creation of novel chemical variants with improved predicted performance. An internal database of optimized candidates is automatically assembled and filtered through ADMET criteria to ensure favorable drug like properties.

The workflow converges on a focused set of final candidates that are computationally optimized for potency, selectivity, and developability. By unifying artificial intelligence, molecular physics, and data driven design in a single platform, our technology delivers a faster and more reliable path from data to drug candidates.