Atomwise Raises $45M by Using AI to Improve Drug Discovery

Atomwise, that uses deep learning for shortening the process of discovering drug discovery of new drugs has raised $45 million Series A. Monsanto Growth Ventures raised the round; Data Collective (DCVC) and B Capital Group. Baidu Ventures, Tencent, and Dolby Family Ventures were also included. All of these ventures are new investors in Atomwise, that have also participated and there were also some returning investors like Y Combinator, Khosla Ventures, and DFJ.

This means Atomwise that was founded in the year 2012 has raised more than $51 million in funding. The company that aims to reduce the amount of money and the time that researchers spend on finding the compound for medications, says that it now has more than 50 molecular discovery programs. However, Atomwise technology is being widely used for developing safer, more effective agricultural pesticides.

Monsanto Growth Ventures partner Dr. Kiersten Stead said in a press statement that “We chose to invest based on the impressive results we saw from Atomwise in our own hands. Atomwise was able to find promising compounds against crop protection targets that are important areas of focus for agrochemical R&D.”

The Atomwise software is known for analyzing the simulation of molecules that help in reducing the time that researchers spend on synthesizing and testing compounds. The company states that it is screening more than 10 million compounds each day. Atomwise’s AtomNet system utilizes deep learning algorithms for analyzing molecules and predicting how they might act in the human body that includes their potential efficiency as medication, side effects, and toxicity at a quite earlier stage than in the traditional drug discovery process.

Atomwise chief executive officer Dr. Abraham Heifets told TechCrunch through an email that the company’s vision “is to become one of the most prolific and diverse life science research groups in the world, working at a scale that is truly unprecedented. This is a large Series A and we will use these resources to grow our technical and business organization. We may eventually find ourselves simulating hundreds of millions of compounds per day. The ultimate upshot is more shots on goal for the many diseases that urgently need new treatments.”

Lead optimization “has historically been the most expensive step in the pharma pipeline,” Heifets added, adding that it also has a very high failure rate, with “about two-thirds of projects failing to even make it to the clinic and it takes five and a half years to get that far.”

When Atomwise launched six years ago, the technology it had seemed almost like something out of science fiction. At present, they have roaster of companies that use artificial intelligence and machine learning for analyzing molecules and fix barriers in the drug discovery process that includes the Recursion Pharmaceuticals, BenevolentAI, TwoXAR, Cyclica and Reverie Labs.

Heifets also stated that Atomwise’s main advantages are the large number of projects that it had worked on, that in turn helps in improving the AI systems. The companies client includes four of the biggest top 10 biggest pharmaceutical companies in the United States, that includes Merck, Monsanto, more than 40 major research universities (Harvard, Duke, Stanford and Baylor College of Medicine among them) and some of the biotech firms. Later, he added that Atomwise also differentiates in its focus.

“There have been two distinct problems in drug discovery and these are biology and chemistry, he added.” “If you’re working on biology, you’re trying to decide which disease protein is the best one to target. A lot of AI companies in drug discovery are working on this target identification problem. Once you’ve chosen a target, you can start working on chemistry problems: how to deliver a non-toxic molecule that can hit the chosen disease protein. Atomwise is focused on these chemistry problems; specifically, Atomwise invented the use of deep neural networks for structure-based drug design.”

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