Alex Zhavoronkov: Linking Chemistry and Biology Using Artificial Intelligence

The team at Insilico Medicine has recently reported that they achieved a major milestone for their comprehensive artificial intelligence (AI)-based drug discovery system: a novel molecule for a novel target discovered with their AI demonstrated efficacy in a broad therapeutic area and reached preclinical candidate stage in Idiopathic Pulmonary Fibrosis (IPF).

Insilico Medicine is a Hong Kong-based company that pioneered the application of deep learning, specifically -- generative adversarial networks (GANs), for drug discovery and generative chemistry.

Dr. Alex Zhavoronkov, Co-founder and CEO of Insilico Medicine, agreed to answer several questions about his path into biotech, the company, the discovery, and how they managed to build an end-to-end AI system capable of uncovering novel targets and molecules, linking biology and chemistry into an integral research process.

This interview text has been edited for clarity and size. Watch the original video for the complete interview.

Andrii: Alex, let’s start with some personal story. You are from a mixed background combining computer science and biotech, and you held a number of executive positions in tech companies. Then you switched to biotech and started Insilico Medicine, can you tell a bit more about how you got where you are today?

Alex: I did my first two bachelor's degrees in Canada at Queen's University -- one at Queen's School of Business, and another one in life and sciences and computer science. Then I worked for a number of semiconductor companies and I also worked at ATI Technologies, which makes graphics chips and which competes with NVIDIA. Now it is part of AMD and back then it was actually much more competitive with NVIDIA than it is right now. Today, NVIDIA is a dominant force on GPUs, powering the deep neural networks that we are currently using. Also, very early in my career, I managed to make some money on the stocks in semiconductors, enough money to sustain myself for a few years. I thought, “OK, what am I gonna do next?”. “Should I continue a rat race of making money, or better do something more impactful?”.

One of my major interests for a long time was aging research. I kind of don’t want to accept the grim vicious cycle where people grow, reproduce, and gradually decline and die. If you think about the marginal contribution of curing any single disease, like for example if you completely cure cancer -- if you eliminate just cancer -- it would add 2.6 years to life expectancy in the United States, that's the current consensus estimate, so it is actually a very marginal increase. It does not really change the picture and you need to look deeper and look at aging. Anyway, I decided to go into biotech and apply the computer science approach to problem-solving to try to deconvolute the very complex process into many fewer complex processes and try to see if we can affect some of those processes to make a bigger impact.

I did my grad work at Moscow State University and at Johns Hopkins, and I then worked for a number of biotech companies and then started my own lab in academia in cancer biology and cancer bioinformatics, I started applying aging research concepts looking at data longitudinally, comparing biology at different ages and trying to train different algorithms to predict age and track age using biological data to see if we can train machine learning systems for age prediction and then see if we can deconvolute that knowledge of human biology of aging into something that is more related to disease and if we can find some relevant targets or pathways. It was kind of a shotgun approach. Also in between, we looked at different biological processes that transpired during aging and published a number of papers on the topic. Then in 2013, I started focusing on deep learning, after visiting an annual graphics technology conference organized by NVIDIA. I knew quite a bit about neural nets at that time, but it was only the beginning of the deep learning revolution – notably after neural networks demonstrated amazing accuracy in image recognition competition Imagenet in 2012. At that time, I also realized if deep neural networks were so good at image recognition why don't we use them for age prediction and maybe then we can look at various ways to derive biologically relevant features.

We started developing a range of algorithms that could reduce the dimensionality of data and allow us to train deep neural networks on smaller data sets to identify the most relevant features and by 2014 we had quite a bit of experience on that and we started the company. So, Insilico Medicine was originally founded as a target discovery company from the perspective of aging research and age-associated diseases. Later we expanded into chemistry and then combined chemistry and biology to get where we are today, discovering novel targets and novel therapeutics.