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PostEra Closes $24 Million in New Financing to Advance Drug Discovery

PostEra, a biotechnology company specializing in machine learning for preclinical drug discovery, announced $24 Million in equity financing to accelerate medicinal chemistry and bring new cures to patients.

PostEra will use the proceeds to advance a series of partnered drug discovery programs, initiate its own internal drug discovery, and further develop its medicinal chemistry platform. True to its collaborative ethos, the company today also announced its multi-target strategic partnership with Pfizer, to establish an AI Lab.

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The Clermont Group, an international business group headquartered in Singapore, led the Series A financing with participation from Breyer Capital, Lifeforce Capital and I2BF Global Ventures, alongside existing investors including Metaplanet, HOF Capital, Ace & Company, Pioneer Fund, and R. Martin Chavez.

“PostEra is excited to welcome the Clermont Group as a shareholder,” said Aaron Morris, CEO of PostEra. “We share the same passion for innovation and commitment to improve lives by accelerating the discovery of treatments for diseases.”

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“We invest in businesses dedicated to the greater good of society,” said Sean Gardiner, senior vice-president at the Clermont Group. “PostEra is at the leading edge of advancing medicinal chemistry through its advanced AI technology. We look forward to working alongside the team to build the industry’s leading drug discovery platform.”

Over the last 18 months, PostEra has proven its platform on a wide variety of drug discovery programs by tightly integrating the Design-Make-Test cycle of medicinal chemistry. Results include the rapid development of COVID oral antiviral candidates via COVID Moonshot, the advancement of several drug discovery efforts with biopharma partners, and the launch of Manifold as an industry leading synthesis platform.

“PostEra has made large strides in demonstrating the real utility of our AI-first medicinal chemistry platform to accelerate small molecule drug discovery,” said Morris. “We have a proven record in deploying our machine learning technology to unify the Design-Make-Test cycle of medicinal chemistry and, vastly improve upon traditional trial-and-error based approaches.”

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