Profluent Utilizes AI for Medicine Discovery: A Venture Backed by Jeff Dean and Inspired by Salesforce Research
Last year, Salesforce, recognized primarily for its cloud sales assistance software (and Slack), initiated a project dubbed ProGen with the objective of designing proteins utilizing generative AI. As a research venture, ProGen could potentially help discover medicinal therapies at a lower cost than the conventional techniques, according to the researchers who mentioned this in a blog post in January 2023.
ProGen ended in research which was published in Nature Biotech journal, showing that the AI was capable of creating the 3D structures of synthetic proteins. But beyond this research paper, the ProGen project didn’t translate much into practical commercial use at Salesforce or anywhere else.
However, that seems to be changing now.
Ali Madani, one of the key researchers from ProGen, has founded a company named Profluent. Madani hopes that his company will take similar protein-producing technology from the research lab to the pharmaceutical companies, and in an interview with TechCrunch, he discussed how Profluent aims to “reverse the drug development paradigm”. This will involve starting from patient and therapeutic requirements and then working backwards to create bespoke treatment solutions.
“Many drugs — enzymes and antibodies, for example — consist of proteins,” Madani said. “So ultimately this is for patients who would receive an AI-designed protein as medicine.”
While at Salesforce’s research division, Madani found himself drawn to the parallels between natural language (e.g. English) and the “language” of proteins. Proteins — chains of bonded-together amino acids that the body uses for various purposes, from making hormones to repairing bone and muscle tissue — can be treated like words in a paragraph, Madani discovered. Fed into a generative AI model, data about proteins can be used to predict entirely new proteins with novel functions.
With Profluent, Madani and co-founder Alexander Meeske, an assistant professor of microbiology at the University of Washington, aim to take the concept a step further by applying it to gene editing.
“Many genetic diseases can’t be fixed by [proteins or enzymes] lifted directly from nature,” Madani said. “Furthermore, gene editing systems mixed and matched for new capabilities suffer from functional tradeoffs that significantly limit their reach. In contrast, Profluent can optimize multiple attributes simultaneously to achieve a custom-designed [gene] editor that’s a perfect fit for each patient.”
It’s not out of left field. Other companies and research groups have demonstrated viable ways in which generative AI can be used to predict proteins.
Nvidia in 2022 released a generative AI model, MegaMolBART, that was trained on a data set of millions of molecules to search for potential drug targets and forecast chemical reactions. Meta trained a model called ESM-2 on sequences of proteins, an approach the company claimed allowed it to predict sequences for more than 600 million proteins in just two weeks. And DeepMind, Google’s AI research lab, has a system called AlphaFold that predicts complete protein structures, achieving speed and accuracy far surpassing older, less complex algorithmic methods.
Profluent is training AI models on massive data sets — data sets with over 40 billion protein sequences — to create new as well as fine-tune existing gene-editing and protein-producing systems. Rather than develop treatments itself, the startup plans to collaborate with outside partners to yield “genetic medicines” with the most promising paths to approval.
Madani asserts this approach could dramatically cut down on the amount of time — and capital — typically required to develop a treatment. According to industry group PhRMA, it takes 10-15 years on average to develop one new medicine from initial discovery through regulatory approval. Recent estimates peg the cost of developing a new drug at between several hundred million to $2.8 billion, meanwhile.
“Many impactful medicines were in fact accidentally discovered, rather than intentionally designed,” Madani said. “[Profluent’s] capability offers humanity a chance to move from accidental discovery to intentional design of our most needed solutions in biology.”
The Berkeley-located company, Profluent, with a team of 20, has received backing from major VC contributors including Spark Capital, who led the firm’s $35 million funding round, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures. Notably, Google’s chief scientist, Jeff Dean has also invested in Profluent, which adds further endorsement to the platform.
In the upcoming months, Profluent will focus on improving its AI model, part of which involves expanding the training datasets, says Madani. The company will also prioritize customer and partner acquisition. There is need for quick action as competitors such as EvolutionaryScale and Basecamp Research are already fast at work, training their protein-generating models and raising significant VC funding.
“We’ve developed our initial platform and shown scientific breakthroughs in gene editing,” Madani said. “Now is the time to scale and start enabling solutions with partners that match our ambitions for the future.”
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