Last year, Salesforce, the company best known for its cloud sales support software (and Slack), spearheaded a project called ProGen to design proteins using generative AI. A research moonshot, ProGen could — if brought to market — help uncover medical treatments more cost effectively than traditional methods, the researchers behind it claimed in a January 2023 blog post.

ProGen culminated in research published in the journal Nature Biotech showing that the AI could successfully create the 3D structures of artificial proteins. But, beyond the paper, the project didn’t amount to much at Salesforce or anywhere else — at least not in the commercial sense.

That is, until recently.

One of the researchers responsible for ProGen, Ali Madani, has launched a company, Profluent, that he hopes will bring similar protein-generating tech out of the lab and into the hands of pharmaceutical companies. In an interview with TechCrunch, Madani describes Profluent’s mission as “reversing the drug development paradigm”: starting with patient and therapeutic needs and working backwards to create “custom-fit” treatments solution.

“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.”

Berkeley-based, 20-employee Profluent is backed by VC heavy hitters including Spark Capital (which led the company’s recent $35 million funding round), Insight Partners, Air Street Capital, AIX Ventures and Convergent Ventures. Google chief scientist Jeff Dean has also contributed, lending additional credence to the platform.

Profluent’s focus in the next few months will be upgrading its AI models, in part by expanding the training data sets, Madani says, and customer and partner acquisition. It’ll have to move aggressively; rivals including EvolutionaryScale and Basecamp Research are fast training their own protein-generating models and raising vast sums of VC cash.

“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.”

Last year, Salesforce, the company best known for its cloud sales support software (and Slack), spearheaded a project called ProGen to design proteins using generative AI. A research moonshot, ProGen could — if brought to market — help uncover medical treatments more cost effectively than traditional methods, the researchers behind it claimed in a January 2023 blog post.

ProGen culminated in research published in the journal Nature Biotech showing that the AI could successfully create the 3D structures of artificial proteins. But, beyond the paper, the project didn’t amount to much at Salesforce or anywhere else — at least not in the commercial sense.

That is, until recently.

One of the researchers responsible for ProGen, Ali Madani, has launched a company, Profluent, that he hopes will bring similar protein-generating tech out of the lab and into the hands of pharmaceutical companies. In an interview with TechCrunch, Madani describes Profluent’s mission as “reversing the drug development paradigm”: starting with patient and therapeutic needs and working backwards to create “custom-fit” treatments solution.

“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.”

Berkeley-based, 20-employee Profluent is backed by VC heavy hitters including Spark Capital (which led the company’s recent $35 million funding round), Insight Partners, Air Street Capital, AIX Ventures and Convergent Ventures. Google chief scientist Jeff Dean has also contributed, lending additional credence to the platform.

Profluent’s focus in the next few months will be upgrading its AI models, in part by expanding the training data sets, Madani says, and customer and partner acquisition. It’ll have to move aggressively; rivals including EvolutionaryScale and Basecamp Research are fast training their own protein-generating models and raising vast sums of VC cash.

“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.”