Singapore and Silicon Valley-based startup, Engine Biosciences, renowned for its utilisation of machine learning and high-throughput biology in the development of precision oncology medicines, has successfully concluded a US$27 million Series A extension.
Engine Biosciences, a Singapore and Silicon Valley-based startup that employs machine learning and high-throughput biology for precision oncology medicine development, has successfully completed a US$27 million Series A extension, led by Polaris Partners. This extension brings the total funding since the company’s inception to US$86 million, with the original Series A now totalling US$70 million.
Among the noteworthy investors participating in this round are existing backers ClavystBio, a life sciences venture investor backed by Temasek, Invus, and EDB Investments, a global investor based in Singapore. Additionally, Engine Biosciences welcomed new investors, including Coronet Ventures, a Singapore-based investment entity associated with Cedars Sinai Intellectual Property Company, and SEEDS Capital, the investment arm of Enterprise Singapore. As part of this financing initiative, Wen Qi Ho, Ph.D., Therapeutics Lead at ClavystBio, has joined Engine Biosciences’ Board of Directors.
Engine Biosciences possesses two unique platforms, NetMAPPR, a machine learning-enabled network biology platform, and CombiGEM, a tool for combinatorial genetics experimentation. These platforms are tailored to facilitate the discovery and optimisation of precision medicines for clinical developers and drug hunters.
With the fresh infusion of capital, Engine Biosciences plans to accelerate its endeavours in biomarker and target discovery, charting a course towards clinical applications. The company aims to achieve these milestones through internal development, strategic collaborations, and mutually beneficial partnerships.
Engine Biosciences integrates machine learning, functional genomics, and drug discovery to enable and develop new and impactful precision medicines. Their capabilities span vast and curated knowledge and data bases on oncology gene interactions and synthetic lethality, proprietary combinatorial CRISPR screens, machine learning-based predictive algorithms extensively trained and iteratively enhanced by internal validation data, thorough validation across ranges of tools and models, drug discovery chemistry, and drug development.
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