InGenio brings target identification, molecule generation, virtual screening, ADMET prediction, and drug repurposing together in one integrated platform — helping researchers explore more candidates, faster.
$2.6B
Avg. cost to bring a drug to market
12+ years
Traditional discovery timeline
90%
Clinical trial failure rate
5
Integrated AI capabilities
Every step of early-stage drug discovery, integrated and automated.
Find druggable protein targets using knowledge graphs, PubMed literature mining, and network analysis. Ranked by association strength, network centrality, and druggability.
Design novel drug-like molecules with fragment recombination, scaffold hopping, and target-optimized Vina-guided hill climbing. Diversity-filtered for structural variety.
Screen compound libraries against targets using fingerprint similarity, Uni-Mol 3D embeddings, and real AutoDock Vina docking with AlphaFold structures.
Predict absorption, distribution, metabolism, excretion, and toxicity. Flags hERG risk, CYP inhibition, AMES mutagenicity, and Lipinski violations.
Find new therapeutic uses for existing approved drugs via network pharmacology, knowledge graph path analysis, and structural similarity scoring.
Chain any combination of capabilities into automated workflows with a drag-and-drop React Flow canvas. Save, share, and re-run pipelines.
From disease hypothesis to validated drug candidates
Type a disease name like 'Alzheimer Disease'. The platform searches the knowledge graph and PubMed to identify druggable protein targets.
Ranked protein targets with evidence scores from genetic associations, protein interaction networks, and scientific literature. Each links to its AlphaFold 3D structure.
AI generates novel molecules designed to bind your targets using known ligand scaffolds. Simultaneously screens the ChEMBL library for existing compounds.
Top candidates are docked against AlphaFold protein structures using AutoDock Vina. Binding scored with Uni-Mol 3D embeddings + Vina energy + fingerprint similarity.
Candidates pass through absorption, distribution, metabolism, excretion, and toxicity filters. Flagged risks (hERG, CYP, AMES) are highlighted automatically.
Final output: a ranked list of drug candidates with target, binding score, ADMET profile, 2D/3D structure, and PubMed evidence. Ready for synthesis decisions.
Integrating the most trusted databases in drug discovery