PHILADELPHIA, PA — Latus Bio, Inc. unveiled a significant expansion of its artificial intelligence and machine learning program as the company pushes deeper into AI-driven AAV capsid and payload design. The move positions the Philadelphia-based firm to accelerate gene therapy development by pairing advanced computational modeling with one of the most extensive in vivo datasets in the industry.
Latus’ strategy centers on training predictive models using data from nine non-human primate screening campaigns, representing more than 100 million “delivery zip codes” — route, capsid, tissue, and cell-type combinations. Leadership says this data-first approach gives the company a structural advantage over traditional AI-first methods, which often lack corresponding in vivo depth.
CEO P. Peter Ghoroghchian said the integration of AI/ML will help Latus sharpen discovery timelines and open new therapeutic possibilities. He noted that by combining multidimensional biological data with generative modeling, the company aims to unlock delivery tropisms beyond common targets like liver, muscle, and eye, long viewed as the industry’s most tractable tissues.
As part of the expansion, Latus added two prominent scientific advisors: Dr. Pranam Chatterjee of the University of Pennsylvania and Dr. Philip M. Kim of the University of Toronto. Both are leading figures in the development of large language and structural generative models for biological design. Their expertise spans synthetic biology, protein engineering, peptide design, and high-throughput experimental validation.
The company’s AI/ML initiative will focus on three primary areas: engineering capsids with precise tissue and cell-type specificity; designing regulatory elements and optimized transgene constructs; and tailoring therapeutic payloads for Latus programs and partners. Executives say the models will guide capsid–tissue prioritization, reveal sequence-function relationships that conventional screening might miss, and improve both scale and predictability in discovery workflows.
Dr. Chatterjee called Latus’ dataset “exactly where AI can make the biggest impact,” pointing to the company’s unbiased, data-rich foundation. Dr. Kim highlighted the opportunity to pair computation with real in vivo validation, describing it as a rare chance to drive meaningful advances in delivery science.
The expanded strategy underscores a growing trend in genetic medicine, as companies race to reduce discovery cycles, improve precision, and address long-standing bottlenecks in AAV targeting. With a strengthened scientific advisory bench and an AI-augmented platform, Latus Bio is positioning itself to compete in an increasingly data-driven era of gene therapy development.
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