Belfast-based startup AMPLY has raised £900,000 in a joint equity and grant funding round to develop new products that tackle global disease threats from multi-drug resistant pathogens using its AI drug discovery platform.
Founded as a spinout from the Queen’s University Belfast in 2021, AMPLY’s AI drug discovery platform was developed to ensure new generations of antibiotics remain effective against infections.
Antimicrobial resistance (AMR) is the process in which bacteria and viruses grow resistant to medication, rendering previously effective treatments useless. AMPLY is harnessing AI to discover alternative medications that are anti-infective.
“It is exciting to be able to advance the commercial development of AMPLY Discovery thanks to the tremendous support of our investors and Queen’s University Belfast,” said AMPLY co-founder and CEO Dr Ben Thomas.
“The development of initial research in this domain started out almost a decade ago when I considered adopting AI and machine learning techniques, I had worked with in financial markets for the computational biology domain.”
The fresh funding for the biotech startup came from the Co fund NI, a £47.3m investment fund managed by Clarendon and backed by Invest Northern Ireland and British Business Investments.
Additional funding came from the QUBIS Innovation Fund for spinouts of the Queen’s University Belfast, the Helix Way Partnership, and angel investments from members of the Halo Business Angel Network.
“AMPLY Discovery is a fantastic example of the support Queen’s can offer our researchers through our investment arm, QUBIS Limited,” said Anne Dornan, entrepreneurial networks manager at QUBIS.
The AMPLY team have moved through our entire gambit of support including the Lean Launch and ICURe programmes.”
In June, QUBIS participated in the £2m funding round for AI medical software startup Sonrai Analytics, which also received support from the Co Fund NI.
AMPLY isn’t the only British startup using AI for drug discovery. Cambridge-bassed Healx uses algorithms to find links between diseases that already have treatments and those that do not for the world’s rarest diseases.
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