"With Cyclica, they are not invested in one biotech company, they are invested in a more neo-biotech company or a technology enabled biotech accelerator that is creating and contributing scientifically to many biotech companies."

What happened in computational chemistry that lead to the emergence of AI and why has Cyclica chosen this approach?

A large percentage of the industry has focused on the problem of creating more efficiency in a target centric approach and several companies were founded with a single target focus. At Cyclica we decided to flip the problem on its head and instead of going narrow on one target, we look at all the potential targets in the proteome and design and evaluate those targets for a given molecule. This made us the pioneers of polypharmacology when it comes to computational chemistry. There is so much more that we need to consider than whether a drug binds to a target. We need to consider the off targets, the absorption, distribution, metabolism, excretion and toxicity. We need to consider synthetic accessibility and novelty chemistry.

Cyclica recently announced its Series B funding. How did you go about persuading investors to back you?

The value proposition is that generally investors are challenged by making a single biotech investment - this requires a lot of capital and substantial risk depending on the phase of the biotech company. With Cyclica, they are not invested in one biotech company, they are invested in a more neo-biotech company or a technology enabled biotech accelerator that is creating and contributing scientifically to many biotech companies. That immediately aligned into the minds of the VC ecosystem.

What are the broader trends you are seeing today from computational biology companies raising money?

In computational chemistry everybody has their own drug design platform now. The industry grew from 15 companies to 200 companies in five years. There is substantial interest in what is happening in the space. I also believe companies are raising money with incremental innovation in areas that have largely been addressed, where investors just want to get in. I have not seen anything drastically novel over the past 18 months. It is just new technology that is going to do something that I think a lot of other companies have the capability of doing. We fully subscribe to getting the fundamentals in place and making sure we position ourselves to mitigate the inevitable Gartner hype cycle crash by rising above and the only way to rise above is to show the demonstrable and sustainable value of the platform that we built.

What are some of the biggest challenges or failures that you have had in growing Cyclica?

Human capital management has been a substantial challenge. Of our 40 employees, 25 are PhDs. They are deep technical experts who came from labs where they had done one thing or a couple things exceptionally deep over time and now we are saying take that into context and look wide in a more holistic paradigm. That became a challenge and early on in our evolution as a company we did not manage or harness the opportunity of cohesion and harmony of the culture and things were spread and scattered. Our technology did not develop nearly as fast as it should have and our business model was scattered. We said the wrong things to the marketplace because there was no cohesion in our messaging. As a result, we were not taken seriously. People would not invest in us. It was a difficult time but we worked really hard at understanding the fundamentals and correcting for it to get where we are now.

What will Cyclica look like as a company in three years and beyond?

We have a lofty goal of creating and thus owning over 300 programs in the next few years. That will be the largest single portfolio of assets in the biotech industry that we know of to date, and we can achieve that through our computational scale and the way in which we build out the company. Our ultimate vision is to then progress novel assets into the clinic and to get them into the hands of patients, many of whom right now are being ignored because their disease is too small to justify the financial investment. We are able to go after these rare and ultra rare diseases and undruggable targets that have largely been ignored because we have the ability to move fast. Therefore, the ultimate goal is to advance better, more efficacious, safer molecules into the clinic, and ultimately to the patients who need them.