Ainnocence utilizes a self-evolving AI drug design platform that performs rapid virtual screening and multi-objective optimization for small molecules and complex therapeutics, achieving computational screening of up to 10 billion compounds in hours. This technology significantly reduces drug discovery time and costs by up to 80%, enabling biotech and pharmaceutical companies to pursue ambitious therapeutic targets more effectively.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional drug discovery processes are time-consuming and expensive, often requiring extensive wet lab experimentation and iterative optimization. Identifying promising drug candidates and optimizing their properties can take years and require screening vast libraries of compounds. Many therapeutic targets remain "undruggable" due to the limitations of conventional methods.
Solution
Ainnocence offers an AI-driven drug design platform that accelerates the discovery and optimization of small molecules, antibodies, and cell therapies. The platform uses deep learning and reinforcement learning algorithms trained on millions of bioactivity data points to perform rapid virtual screening of billions of compounds. This approach enables multi-objective optimization of drug candidates, including ADME/Tox properties, target binding specificity, and developability, significantly reducing the need for extensive wet lab iterations. Ainnocence's platform expands the druggable target space and reduces drug discovery time and costs.
Target Audience
Ainnocence primarily targets biotech and pharmaceutical companies seeking to accelerate drug discovery, reduce R&D costs, and pursue previously undruggable targets.
Features
- CarbonAI: A small molecule design engine for de novo compound generation, lead optimization, and PROTAC design, capable of screening billions of compounds in days.
- SentinusAI: A protein design engine for de novo antibody and fusion protein engineering, affinity maturation, humanization, and epitope mapping.
- CellulaAI: An AI engine for optimizing CAR-T therapy, including target antigen identification, scFv design, and off-target screening.
- Multi-objective optimization: Simultaneous optimization of pharmacological properties, including ADME/Tox, target binding, and developability.
- Integration of 100+ data sources: Cleansed and integrated data for AI training.
- High hit rates: 10%-60% hit rate on wet lab validation.
- Reduced wet lab iteration: Reduces the number of candidate molecules needed for wet lab testing.