OutSee, a genomics and drug discovery company pioneering a unique AI-based predictive genomics approach to target discovery, announced the closure of its £2.5 million seed funding round. The round was led by Ahren Innovation Capital, with Kadmos Capital, Panacea, Empirical Ventures, and 26 independent angel investors also participating.
The company also announced the signing of a strategic agreement with o2h discovery, a preclinical Contract Research Organisation with an integrated drug discovery platform. The partnership aims to launch a collaborative drug discovery programme for OutSee’s lead target candidate, identified through the company’s genomic target discovery and precision medicine platform, Nomaly.
Funding and support for the partnership has been provided through o2h’s co-discovery InflexionTx, a match funding model at the HIT ID Phase covering critical proof of concept studies, which aims to unlock novel high value ideas and assets. Integrating OutSee’s computational biology expertise, the research project is focused on building a library of drug candidates for OutSee’s lead target ahead of future therapeutic development.
The first close of OutSee’s seed funding was announced in June 2025 and now completes at £2.5 million. Since the first close, the company has expanded its in-house discovery programme, using its Nomaly platform to identify a portfolio of therapeutic targets. The funding will enable the company to advance these targets through experimental validation, generate an internal pipeline of assets, and continue R&D of the Nomaly platform to enhance the core technology and transition from validation to active value generation.
The Nomaly platform adopts a ‘genomics first’ approach to target identification. Using an AI-powered engine based on hypothesis-free predictive modelling, Nomaly generates conclusions from molecular and cellular biology of the genome, enabling disease and phenotype prediction directly from a single genome. The approach is complementary to existing target discovery pipelines and is applicable to small datasets or those previously analysed using conventional methods.
