New Startup Formed by Israel-Based Venture Studio Leverages AI to Accelerate Discovery Process for More Effective Biological Therapeutics for a Range of Diseases
AION Labs, the first-of-its-kind venture studio spearheading the adoption of AI technologies and computational science to solve therapeutic challenges, announced the launch of CombinAble.AI. The new startup has been created to address the challenges associated with antibody design in order to enhance and accelerate the development of more effective therapeutics.
CombinAble was formed following an AION Labs’ startup challenge aimed at developing an AI solution for the optimization of therapeutic antibodies. The company is addressing this problem by developing a platform that integrates cutting-edge AI technology and state-of-the art computational methods for biomolecule simulations. By automating the selection of mutations that synergistically enhance multiple biophysical characteristics of an antibody candidate, the platform is expected to facilitate the discovery and design of a new biotherapeutic, making the process faster and more effective.
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“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,” said Daria Kokh, PhD, Co-Founder and CEO of CombinAble. “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.”
Antibody optimization is a critical step in the drug development pipeline, transforming a preliminary functional antibody hit into a clinical drug candidate by enhancing multiple characteristics related to its safety, efficacy, and manufacturability. It traditionally involves a sequential and time-consuming trial-and-error process entailing multiple wet lab validation steps. This progression, from basic physical and chemical properties to more complex pharmaceutical attributes, can take years and incur high costs with no guarantee of success. The transition from theoretical models to practical applications introduces further complexity, making replicating success at the molecular level challenging and potentially leading to setbacks in later stages of development.
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CombinAble adopts a distinct approach by allowing a model to “learn” from data and physics-based principals in order to find the best solution in one shot. The company is developing a comprehensive AI-based platform for automating the modification of antibody sequences to simultaneously enhance a range of characteristics essential for more effective therapeutics. This involves leveraging data provided by AION Labs’ pharmaceutical partners, in addition to publicly available datasets. Such an approach represents a paradigm shift, transitioning antibody discovery from the traditional process of trial-and-error to a single in-silico solution.
CombinAble is the latest startup launched by AION Labs, through the investment and support of global pharmaceutical and technology leaders. Its founding team comprises Daria Kokh, PhD, as CEO and Noam Katz, PhD, as AI and R&D Lead. Dr. Kokh, a physicist and expert in biomolecular modeling and simulations, previously held senior positions at the Heidelberg Institute for Theoretical Studies and the Cancer Registry of Baden-Württemberg. Dr. Katz is an experienced data scientist, previously serving as Data Lead at Iluria and Head of Research for BreathVision.
“The launch of CombinAble heralds a shift in the approach to targeted antibody design by leveraging AI to optimize and expedite the discovery process,” said Mati Gill, CEO of AION Labs. “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.”
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