BEYOND Expo | How AI can accelerate drug research and development

As AI technology becomes a ubiquitous invisible force in our daily life, traditional fields such as medical research and development have embraced the technology to speed up processes and change the industry.

Wen Shuhao, the founder of AI-powered drug developer XTalPi, said, “the door for the digitalized drug industry, intelligent automation, and AI-driven drug development has opened.”

Wen shared his views on how AI works in the process of drug research and development and discussed what startup they would invest in a fireside chat at the BEYOND Expo 2022, held online at BEYOND Metaverse on Sept. 23.

Using AI in drug discovery and development

First, we need to know what AI is before we figure out what AI drug discovery is. From our perspective, AI is all about collecting, connecting, computing capacity, and sharing data worldwide.

In terms of AI drug discovery, it’s all about using AI algorithms to help us understand drug structures such as AlphaGo 2.

Compared to traditional chemistry, looking at hundreds of millions of molecules, we can do many properties predictions and then form a feedback system, which is what AI can help us with.

That’s our interpretation of the essence of AI drug discoveries. This is a new paradigm instead of relying on humans for drug discovery. We can use huge computing power and algorithms for more effective drug discovery and development.

[We] hold great expectations in artificial intelligence for drug research and development. AI in the drug industry is an inevitable trend. The door for the digitalized drug industry, intelligent automation, and AI-driven drug development has opened. It does not happen overnight but is a process.

XTalPi is pretty lucky. Most of the leading companies in this industry started in 2015. In the past couple of years, we raised about $100 million. The top 20 pharmaceutical companies have established collaborative relationships with my company.

AI drug development is about building the most efficient feedback system, not just the algorithm. Whether the score your AI or algorithm produces can be fed back to the AI in the most efficient experimental way matters too.

What startup would you invest in?

We want to lower the barrier to drug development. So we want to be able to help more biological and farming companies so that they can develop more drugs and benefit the entire industry.

And to do that, we were able to develop and incubate many ecosystem partners so that we can also give feedback to the industry and get returns from this.

And there are a lot of unmet needs in the industry. For example, we helped two companies — one work on self-immunity and another on stomach cancer — [on fundraising.] Both are small startups with huge market value and facing difficulties accessing early rounds of funding. Within a year, we help them to get funding. I think they are doing great things, and their founders also have great expertise and much experience in medical practice and have published five or six articles in Nature.

And they are also the first top-ranking researchers in their specialized fields, so we can help them scale. And also another company is doing AI medicine to develop a delivery system. So within three years, they gained several rounds of funding, and they could grow 50 times or 100 times faster than before. And we’re able to develop and help to grow these companies.

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