#BIO2022 

Ainnocence, a global biotech startup on a mission to accelerate drug discovery using a fast, self-evolving AI drug design platform, launched today at the 2022 BIO International Convention (booth 4001).

Founded by a versatile team of biomedical scientists and computer scientists, Ainnocence provides small- and large-molecule design systems that dramatically lower the costs and risks of drug discovery — bringing the industry closer to curing some of mankind's most intractable diseases.

"We want to equip biotechs and pharmas to tackle moonshot cures that have until now been out of reach due to staggering discovery costs and low ROI. Never before have drug discovery teams had a system of such power at their disposal," said Lurong Pan, CEO of Ainnocence. "If a science team provides us with just the sequence information of their biological targets, we can screen billions of molecules and compounds in just hours, and in many cases completely bypass high-throughput wet-lab experimentation."

Today, Ainnocence is debuting two services that automatically generate highly optimized drug-like molecules within just hours to solve some of the most complex drug discovery challenges:

• SentinusAI™ — a de novo antibody and fusion protein engineering engine that performs antibody design and optimization based solely on sequences. Capabilities include protein-protein binding affinity maturation, protein humanization, off-target toxicity prediction, protein stability and post-translational modification prediction.
• CarbonAI™ — a de novo small-molecule and PROTAC design engine for multi-objective pharmacological profile optimization. Capabilities include target binding and selectivity optimization, molecule generation, ADME and PK evaluation, and compound off-target prediction.

Ainnocence offers a range of small-molecule, biologics, and other new modality drug-design services and pipeline collaborations. These services are powered by InnocentAI™, a self-evolving AI cloud platform that runs natively on AWS and supports multiple drug-design AI modules with unlimited scalability. With its inherent self-learning abilities, the accuracy of this computational platform is constantly improving through an automatically updated data-integration and algorithm-optimization engine.