1Mby1M Virtual Accelerator Investor Forum: With Sergey Jakimov, Co-Founder and Partner at LongeVC (Part 2)

Sramana Mitra: I want to double-click down on a bunch of areas that are broadly relevant to digital health. What you said about applying AI to drug discovery is of interest. Any kind of quantified health measures is also of interest.

The intersection of computer science and biology is where our audience would find the most useful insight. What is the state of the union and where are you playing? What are some of the case studies in your portfolio?

Sergey Jakimov: If we’re talking about AI for drug discovery, the first question is why is it important. It is immensely important because it optimizes so many processes for pharma companies. We are looking at cutting down cost tremendously, but we’re also looking at cutting down the time it takes to come up with a molecule candidate where you’re cutting down two years of lab work down to 40 days.

A very good example from our own practice is the deal that was led by one of our partners. Insilico Medicine is now a unicorn but, six years ago, they were one of the very first ones that allowed for the hypothesis of using artificial intelligence to repurpose drugs or to create new drug candidates. The funding round was led by Alex. He’s Latvian-born as well.

Six years ago, they encountered a very significant adoption barrier because that was very unconventional. There was a lot of pushback. Then the rest is history. Today, most of the big pharma are their clients. The company has transformed from being AI-as-a-Service to running its own comprehensive platform that they sell to pharmaceutical companies and developing their own pipeline of drugs. They started their own clinical trials recently.

AI for drug discovery has been an immense quantum leap in how we automate and how we speed up drug development processes. If you think about it, it takes 10 or more years to get the drug to market. If you cut two years out, you can basically create drug candidates much faster. You can iterate faster. That advances the field tremendously. It’s very data-dependent. It’s the old garbage-in-garbage-out principle. AI for drug discovery has been one of the, if not the, most significant innovation in life sciences.

Sramana Mitra: This company that you’ve invested in that is now a unicorn, how many drugs are going through this technology right now?

Sergey Jakimov: The business models for these companies mostly differ. The go-to business model is selling its AI for drug discovery to pharmaceuticals. This is where you can’t keep track. It’s sold to pharma for research purposes. Insilico has developed its own pipeline of drugs where they already have several drugs on clinical trials.

Sramana Mitra: I was trying to understand more of the industry’s evolution. If the pricing model for your company was per drug, then we would have a sense of how many drugs are going through this kind of technology. I was trying to gauge the maturity of the field.

Sergey Jakimov: The field is not mature at all. I don’t think there is a single AI-developed drug in the market that has been approved by the regulators in all three phases of clinical trials. That still doesn’t exist. I assume the first one will be there in the next few years.

This segment is part 2 in the series : 1Mby1M Virtual Accelerator Investor Forum: With Sergey Jakimov, Co-Founder and Partner at LongeVC
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