Deep Origin’s computational discovery systems model life so researchers can close the gap between preclinical predictions and clinical outcomes. By combining physics-based simulation with AI, we make predictive biology actionable across scales: from quantum chemistry and protein-ligand binding to cellular pathways, whole-cell physiology, and ultimately the human body. The result is physics-grade accuracy fast enough for daily decisions and broad enough to reach targets and modalities that pure machine learning cannot.
Our systems have outperformed industry benchmarks across the discovery stack. In hit discovery, Deep Origin identified 48 CD73 hits from 159 compounds tested, a 30% hit rate versus a published industry benchmark of roughly 1%. In novel target predictivity, DO Dock achieves a 54% success rate on protein-ligand structures with less than 20% similarity to its training set, three to ten times higher than leading AI structure-prediction models. In lead optimization, our hybrid quantum/classical free energy method predicts BRD4 binding affinities with 0.55 kcal/mol mean error, improving on leading results while running roughly ten times faster than comparable hybrid approaches. Our physics-based simulator also predicts targeted protein degrader complexes at roughly three times the accuracy of pure ML, while pathway models predict viability across roughly 1,500 cancer cell lines.