What aspect of the drug discovery process does Atomwise seek to improve?

AI for drug discovery can be broken down into three categories: AI for biology, AI for chemistry and AI for clinical trial design and execution. Atomwise works on AI for chemistry. We were the first group to use the modern machine learning technique of convolutional neural networks for drug discovery.

There are a number of fundamental challenges in the traditional, early-stage drug discovery process. Traditional approaches are notoriously lengthy and complex, often involving years of iterative and complex medicinal chemistry. Atomwise leverages AI for structure-based small molecule drug discovery, removing the barriers of physical screening that have limited the success of traditional methods. By predicting the binding of billions of small molecules to a protein of interest having a known disease association in a matter of days, Atomwise can accelerate the earliest stages of drug design by several orders of magnitude.

Our technology is based on convolutional neural networks – the same AI technology that is used for image and speech recognition. Atomwise was the first group to take what works in image and speech recognition and apply it to molecular recognition. We built an AI system called AtomNet® and ran it in the biggest application of machine learning in drug discovery in history. At Atomwise, we partner with top-100 pharmaceutical and emerging biotechnology companies, a rapidly growing market estimated to reach $729 billion in global market value by 2025, and academic researchers at universities, institutes, and hospitals around the world. We now have over 750 projects, across every major therapeutic area, addressing over 600 unique targets. We have 285 active drug discovery partnerships with researchers at top universities and we’ve maintained an over 75% success rate on these projects.

How does using AI for drug discovery compare with traditional high throughput screening methods?

At Atomwise, we routinely screen libraries of over 16 billion molecules. Atomwise drastically reduces physical screening efforts, helping our partners identify leads without having to synthesize or buy large libraries of compounds.

Chemical vendors are offering about a billion new molecules a month, each of which you can purchase and have shipped to you in four weeks. In contrast, if you screened the entirety of a big pharma corporate collection, you’d have tested only 3-5 million molecules. In other words, if you put together all the big pharma corporate collections, and multiplied that by 10, that is how many new molecules are being added to purchasable chemical space every month. In our experience, these ultra-large libraries yield more, better, and more varied starting points (“scaffolds”) than you can with physical techniques like high throughput screening.  The challenge is that even a modest false positive rate swamps any right answers, so success requires predictive models that are highly performant and deliver exquisite accuracy.

Atomwise recently announced a substantial round of fundraising. How will that money help grow the company?

Our previous funding round let us challenge, refine and test our system, and prove that it works. This Series B round is about taking that system, using it to discover real medicines and get them to patients. We will build our own internal pipeline and continue to grow our portfolio of joint ventures with leading researchers using AtomNet® for drug discovery.

Has Covid-19 expedited adoption of advanced technologies?

The race to develop treatments for Covid-19 has put the drug discovery process under a global microscope - revealing that it takes far too long, costs way too much, and stunts potential positive outcomes due to the physical limitations of testing and screening viable drug candidates.

What key milestone does Atomwise hope to reach moving forward?

We will scale the capabilities of our technology. We have gained a valuable breadth of experimental data, including the largest diversity of drug target sites, homology models, protein classes and disease areas of any AI platform. We must continue to scale our algorithms to keep up with the growth of ultra large libraries of 100 billion molecules and up.

Furthermore, to scale the technology, we are going to need to scale the team. We will continue to scale the collaborations we have been doing, whether that is with big pharma, small pharma or joint ventures. For us, “success” is being able to make the discovery of effective drugs faster, less expensive and make the process more democratic.