Galux Collaborates with Boehringer Ingelheim to accelerate AI-driven protein design
South Korea’s Galux, a biotech startup focused on AI-powered protein therapeutics discovery, has entered a strategic research agreement with Boehringer Ingelheim. The collaboration aims to explore and advance artificial intelligence technologies that can design novel protein therapeutics with improved efficacy, safety, and development timelines. The partnership signals a growing trend among pharmaceutical leaders to leverage machine learning and computational biology to shorten drug discovery cycles and expand therapeutic possibilities.
What the partnership aims to achieve
The joint program centers on applying cutting-edge AI models to protein design challenges, including sequence optimization, structure prediction, and function prediction. By combining Boehringer Ingelheim’s extensive clinical and regulatory know-how with Galux’s expertise in AI-driven discovery, the two organisations intend to create a pipeline that can identify viable protein candidates more quickly than traditional methods. The effort is expected to cover early-stage target discovery, in silico screening, and iterative refinement of protein topologies to enhance binding properties and therapeutic potential.
Why AI is changing protein design
Protein design has historically depended on labor-intensive experimental screening and incremental improvements. Advances in artificial intelligence are enabling researchers to model complex protein folding dynamics, predict binding interactions, and simulate cellular behavior at scale. This reduces the need for exhaustive laboratory trials and helps researchers prioritize high-potential candidates for validation. In this collaboration, AI will be used to generate and optimize protein variants, evaluate drug-like properties, and map potential safety profiles before moving to experimental testing.
Implications for the biotech landscape
For Galux, the alliance broadens the company’s access to industry-grade resources and regulatory guidance, while Boehringer Ingelheim gains a partner with novel AI capabilities to augment its internal discovery programs. The partnership aligns with a broader corporate strategy among major pharma players to integrate artificial intelligence across research and development stages. If successful, the program could shorten discovery timelines, reduce costs, and expand the portfolio of protein therapeutics that address unmet medical needs—ranging from autoimmune diseases to neurodegenerative conditions.
What success could look like
Early milestones may include validated AI-generated protein designs with demonstrable binding affinity and stability in preclinical models. Subsequent steps would likely involve iterative cycles of AI-guided design, experimental validation, and optimization to satisfy pharmacokinetic and safety requirements. The collaboration may also contribute to shared platforms for data, benchmarks, and open science collaboration, accelerating progress across the protein design community.
About the parties
Galux is positioned as a pioneer in AI-driven therapeutics discovery in South Korea, building computational pipelines that accelerate protein engineering. Boehringer Ingelheim is a global leader in human pharma, with a long history of translational research and a broad portfolio spanning cardiovascular, respiratory, metabolic, and oncology therapies. The collaboration reflects a mutual interest in harnessing artificial intelligence to unlock new therapeutic modalities and speed to clinic.
What comes next
Details about the exact scope, milestones, and timelines of the research agreement remain confidential, but the industry will be watching closely for announcements on initial results, shared datasets, and any translational milestones that move AI-designed proteins toward clinical exploration. As regulatory landscapes evolve for AI-assisted drug discovery, partnerships like this could set a precedent for how large biopharma collaborates with nimble biotech startups to push the boundaries of what is scientifically possible.
