LabGenius, a company that uses machine learning (ML) for antibody discovery, has secured £35 million in a Series B funding round. The funding round was led by M Ventures, the strategic venture capital arm of Merck, a leading science and technology company. This investment will fuel the development of their proprietary platform and progress a pipeline of multispecific antibodies towards clinical trials.
Participation in the round also included new investors Octopus Ventures and LG Corp, alongside existing investors like Atomico, Kindred Capital, and Lux Capital. This diverse group of investors brings a wealth of experience and resources to the table, supporting LabGenius’ growth trajectory.
Just last month, we had also reported about AI medtech Qureight that raised $8.5M to develop drugs for lung and heart diseases.
AI-driven drug discovery
LabGenius will utilise the new funds to broaden the capabilities of its ML-driven platform, known as EVA. This platform utilises robotic automation and artificial intelligence to streamline the identification of powerful and unique biotherapeutics, specifically antibodies. By expanding the platform’s scope, LabGenius aims to facilitate broader strategic partnerships across various therapeutic areas.
LabGenius’ focus on multispecific antibodies and overcoming challenges
In addition to platform development, LabGenius will progress a wholly-owned pipeline of multispecific antibodies for treating solid tumours. These antibodies are designed to target specific markers on cancer cells, offering a promising approach for cancer treatment.
A key challenge in antibody development is on-target, off-tumour toxicity. Therapeutic antibodies can mistakenly bind to healthy cells that share similar surface markers as cancer cells, leading to unintended side effects. LabGenius’ platform addresses this challenge by optimising antibodies for tumour selectivity alongside other critical factors like potency and efficacy.
How the startup is leveraging machine learning for efficient antibody design
Traditionally, antibody optimization involves a sequential approach, focusing on individual properties. This method can be inefficient, as improvements in one area might negatively impact others. LabGenius’ platform tackles this challenge with Multi-Objective Bayesian Optimization (MOBO), an active learning technique that allows for co-optimization across multiple properties simultaneously.
This innovative approach not only streamlines the process but also reduces human bias and yields high-performing antibodies with non-intuitive designs. In simpler terms, the AI can identify effective antibody structures that might be overlooked by traditional methods.
What investors are thinking about the company
Dr. Oliver Hardick of M Ventures expressed enthusiasm about LabGenius’ technology, highlighting its potential to revolutionise antibody engineering. He emphasised the co-optimization capabilities of the platform and its potential to address challenges faced by the industry.
Dr. James Field, CEO of LabGenius, reiterated the immense potential of multispecific antibodies and the company’s commitment to developing EVA. He expressed gratitude towards the team’s dedication and looked forward to collaborating with investors to accelerate platform development and clinical trials.
Dr. Edwin Moses, Chairman of LabGenius’ Board, applauded the high-calibre of investors and the validation it brings to LabGenius’ platform and team. He emphasised the company’s commitment to addressing unmet medical needs and rewarding stakeholders for their support.
What we think about the startup
LabGenius’ successful funding round and cutting-edge technology position the company at the forefront of AI-powered drug discovery. Their focus on overcoming challenges in antibody development and progressing a wholly-owned pipeline holds significant promise for the future of cancer treatment.
As LabGenius continues to refine its platform and advance its pipeline, it will be interesting to see how their AI-driven approach transforms the landscape of antibody discovery.
