News

The platform has been applied across 60+ therapeutic programs, including antibodies, small molecules, cell therapies, siRNA, and synthetic biology applications. In one landmark study, Ainnocence computationally designed mutation-resistant antibodies against SARS-CoV-2 variants, successfully predicting neutralizers for Delta and Omicron before Omicron emerged, demonstrating the model’s ability to learn evolutionary patterns directly from sequence data.

Natural molecules remain a foundational source of bioactive compounds, yet their discovery and optimization are constrained by scarcity, structural complexity, and limited tunability. NatmolAI™ addresses these limitations by decoupling molecular design from physical isolation, allowing nature-derived chemical principles to be explored, expanded, and optimized computationally before experimental validation.

CosmeticAI™ shifts early-stage discovery from experimental screening to in silico design. For a given target, the platform generates large populations of candidate molecules and evaluates them using learned relationships between molecular sequence, structure, and function. This enables systematic exploration of molecular space beyond what is feasible with physical libraries, while maintaining explicit control over design objectives.

Using a formula-centered knowledge graph combined with process-level digital twin simulations, FormulaAI™ predicts formulation stability, compatibility, storage behavior and service life directly from compositional and operational parameters. This approach allows teams to eliminate weak candidates early, focus experimental efforts more effectively and reduce reformulation cycles.

Oligonucleotide engineering has traditionally been slowed by challenges in sequence selection, immunogenicity, durability, manufacturability, and delivery compatibility. Ainnocence’s platform addresses these limitations through a sequence-first approach that evaluates millions to billions of candidates in silico, identifying the optimal therapeutic designs long before laboratory testing begins.

Material AI operates on the same computational backbone powering Ainnocence’s platforms in small molecules, biologics and RNA therapeutics. With cross-disciplinary predictive engines, the system supports a wide range of applications, including high-performance alloys, nanomaterials, functional materials for electronics and next-generation energy systems.