The platform will allow for a more direct method of antibody optimization.
The AI revolution continues.
A new start up in Israel plans to improve the ways that antibodies are developed in an attempt to fully optimize the drug development process. AION Labs is developing an AI-powered platform known as CombinAble.AI.1 The platform uses the algorithmic technology to produce high quality biomolecules simulations.
The hope is that this will reduce both the time it takes and the cost of developing new antibodies.
In a press release, CombinAble co-founder and CEO Daria Kokh, PhD, said, “Our emphasis is on combining various advanced machine learning models, including large protein language models, molecular dynamics simulations and multi-objective optimization algorithms, integrating them into a single unified solution for targeted antibody discovery. AION Labs' resources and expertise have been pivotal in shaping our company, providing the material and logistical support to help us refine our scientific strategy, and expand the team. As we progress, the knowledge and insights provided by its pharmaceutical partners will be invaluable for the development and commercialization of our solution."
In the same press release, AION Labs CEO Mati Gill also said, “The launch of CombinAble heralds a shift in the approach to targeted antibody design by leveraging AI to optimize and expedite the discovery process. We are delighted to welcome CombinAble into the AION Labs ecosystem and look forward to working closely with the team to develop solutions that enable the design of highly effective biological therapeutics."
According to the company, the process known as antibody optimization allows them take a newly discovered antibody and then enhance it by adjusting characteristics related to safety, efficacy, and manufacturability. The CombinAble system is able to learn from data and physics-based principles to search out solutions. Hopefully, it will be able to transform the process from a trial-and-error method to a more direct style.
The goal is to be able to automate the process of optimizing antibodies to free up researchers developing or searching for new antibodies.
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