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From left: Converge Bio’s Iddo Weiner, Chief Scientific Officer; Dov Gertz, CEO; Oded Kalev, CTO.Image Credits:Omer Hacohen / Converge Bio
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AI is finding its way into every corner of biotech and pharmaceutic research , but like other industries , it ’s never quite as straightforward to go through as one would like . Converge Biohas built a tool for company to make their biota - focused LLMs really forge , from “ enrich ” their data to explaining their resolution . The party has raised $ 5.5 million in a cum round to scale its product .
“ A role model is just a model . It ’s not enough , ” said CEO and co - beginner Dov Gertz . “ A pipeline has to be made so companionship can actually use the model in their own R&D process . The securities industry is very disconnected , but pharma and biotech want to consume this applied science in a consolidated means , in one place . We want to be that property . ”
If you ’re not a simple machine check engineer working in drug discovery , this may not be a familiar problem to you . But fundamentally , there are powerful foundational models out there , large words models aim not on books and the internet but on vast databases of DNA , protein structures , and genomics .
These are powerful and versatile models , but like the LLM used in products like ChatGPT and Cursor , they require a lot of work to hammer into a anatomy that masses can in reality use day to solar day . That work is particularly difficult in specialized domains like microbiology or immunology . Taking a “ new ” LLM trained on gazillion of protein sequence and making it something a research lab technical school can use as part of their normal inquiry is a non - footling job .
As an example , Gertz suggested antibody research . An LLM discipline on antibody - specific biota exist , but it ’s very general . Converge Bio offers a series of improvements that can be done securely and using a company ’s own IP .
First is “ data enrichment , ” augment the antibody LLM with important related information like antigen - antibody and protein - protein interactions . Then , load with more specific knowledge , it can be delicately - tuned on the specific antigen the team is look to target , and which they may have proprietary in - looker data on .
“ Now we have an covering : The input is a chronological sequence , the production is binding affinity , ” Gertz said . Then the platform provides another important level : explainability . Researchers can drill down on the output to happen out not just that “ this sequence works well than this ” but place down to the amino group acid or base duet stage what part of the sequence seems to bemakingit work better .
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last , it bring forth new sequences that offer improved result , likewise with explainability . Gertz noted that the explainability has surprised them with its popularity among customers — makes horse sense , since it tolerate expert to apply their domain expertise ( say , protein fundamental interaction ) to this newer and more obscure region of bioinformatics and machine learnedness .
Converge use the many open rootage and loose fundament models out there , but is also working on making its own . It already has a proprietary procedure , Gertz say , for the explainability part . And the data point enrichment “ curriculum ” is entirely theirs as well — not a trivial process . Training methodological analysis , he pointed out , are one of a few closely guarded secrets by the most successful AI company .
That ’s part of the moat they ’re hoping to build , along with the fact that , as Gertz put it , “ This is plausibly the biggest opportunity in biotech in five decade . ”
Yet many , perhaps most , biotech companies do n’t have a dedicated solution for doing LLM - related work in their field , and actively pursuing niches that generalist solutions do n’t put on to .
“ The approximation is to be the everything memory board for GenAI in biotech , then use that as a wedge to extend more over time , ” Gertz said . “ The behavior in pharma and bio is , once they have ties to a marketer that they commit , they want to apply them in other use case , be it antibody design or vaccine invention . That ’s why I retrieve this positioning is best for this moment in the market . ”
investor seem to agree , putting $ 5.5 million into a seeded player stave led by TLV Partners .
The company will be using the money to hire up and acquire customers , as startups often do at this stagecoach , but will also be put out a scientific paper on antibody design ( using its own systems , of course ) and train “ a proper foundation example . ”
