Alithea Genomics extends funding to accelerate RNA-seq innovation
Swiss biotech company Alithea Genomics has secured an $8.9 million extension round led by Belgian investor Novalis Biotech, bolstering its mission to make high-throughput RNA sequencing (RNA-seq) faster, more scalable and dramatically more affordable. The fresh capital will be used to industrialise and globally scale the company’s multiplexed RNA-seq technology, a platform designed to support next-generation AI drug discovery, functional genomics and toxicology research.
Cutting RNA-seq costs for AI-driven drug discovery
Alithea Genomics specialises in methods that allow thousands of biological samples to be processed in a single, pooled workflow. By multiplexing samples early in the library preparation step, the company’s technology reduces hands-on time, reagent usage and per-sample sequencing costs. This cost-efficiency is increasingly critical for labs generating massive datasets for machine learning and AI algorithms used in target discovery, compound screening and safety profiling.
Backer Novalis Biotech focuses on ventures that sit at the intersection of biotechnology, data and digital tools. Its lead role in the extension round signals strong investor confidence in high-throughput transcriptomics as a foundational data layer for AI-first pharmaceutical R&D.
Scaling for pharma, biotech and toxicology applications
The new funding will enable Alithea Genomics to expand production capacity, strengthen its commercial team and deepen collaborations with pharmaceutical and biotechnology partners. The company aims to position its multiplexed RNA-seq kits and services as a standard solution for large-scale toxicology screening, cell line characterisation and perturbation studies, where tens of thousands of samples must be profiled consistently and at low cost.
By lowering the cost barrier to comprehensive transcriptomic profiling, Alithea Genomics is targeting a key bottleneck in modern drug development: the need for rich, high-quality biological data to train and validate powerful AI models. As demand for data-intensive approaches grows across the life sciences, the company’s technology is poised to play a central role in enabling scalable, data-driven discovery pipelines.