Ainovo Biotech Inc.
We are a next-generation biotechnology company creating an AI-powered platform for custom protein design. Our platform develops Novobinders: proprietary protein binders with diagnostic and therapeutic applications. AINovo Biotech works with select biopharmaceutical and biotechnology companies to accelerate target selection, to jointly develop novel medicines and optimize therapeutic profiles, and to inform and desrisk clinical strategy. These goals are carried out through our proprietary AINovo Biotech platform including our data engine, CURIUS, which synthesizes diverse and siloed massive datasets, to enable modern machine learning methods to solve major challenges in the development of novel and effective biologics.
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- Nationally (across the country)
AINovo™ Biotech Inc. synthesizes and extracts insights from the world's largest and most diverse biological datasets. By combining massive datasets with our proprietary machine learning methods, rapid experimental iteration, and a highly interdisciplinary team, we are dramatically reducing the time and cost of bringing life-saving treatments to market.
Protein-based therapeutics are at the forefront of biomedical research, and have been used to treat a variety of conditions with unmet medical needs. Biologics such as therapeutic antibodies have transformed the field of oncology and led to transformative cancer immunotherapy treatments.
At the same time, the increase in genomic sequencing, proteomic data, real world evidence, and electronic health records have led to a wealth of available information which can be leveraged to extract relevant pharmaceutical insights. Cutting-edge artificial intelligence algorithms in computer vision, natural language processing, and generative machine learning have matured and can be brought to bear on these curated datasets to enable data-driven decision making in pharmaceutical R&D problems.
AINovo™ Biotech Inc. is leveraging this unique interdisciplinary opportunity afforded by massively growing biomedical datasets, modern machine learning algorithms, and growing unmet medical needs in order to accomplish our mission of enabling data-driven discovery of novel biologic therapeutics and diagnostics for patients.