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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.

The RNA platform is built on the same computational backbone powering Ainnocence’s small-molecule, biologics, and cell-gene therapy solutions. Cross-modality prediction enables multi-target, multi-format design for partners pursuing mRNA vaccines, sequence-based gene regulators, RNA-encoded biologics, and next-generation therapeutic libraries.

Conventional biologics discovery has long been limited to well-behaved targets with known structural properties. Ainnocence’s AI platform removes these boundaries by identifying non-traditional binding interfaces, allosteric mechanisms, and conformational dynamics that are often overlooked in static modeling.