Launch HN: PostEra (YC W20) Medicinal Chemistry-as-a-Service and Covid Moonshot

What is medicinal chemistry? It’s part of discovering new drugs. A drug hunter decides what disease to focus on and then selects ‘targets’: usually proteins whose activity is key to the disease. Then they look for a molecule that can ‘hit’ that target and stimulate a response which will hopefully have beneficial effects. Developing such a molecule that is potent and safe is medicinal chemistry.

Despite it being a crucial part of drug development, this field has relied on trial-and-error approaches—a very expensive way to muddle toward a drug. Where computational tools have been used, they have emphasized the 'best' designs without any awareness of what it would take to physically make the drug in a lab and test it. Our approach is to apply computational methods that know how to make these designs.

We’ve been working on developing machine learning tools to advance the field for the last 3 years. Alpha formed a lab at Cambridge in 2017 to apply machine learning to drug discovery. Matt joined the group and soon some exciting results began to emerge, particularly in the area of how to make molecules. We published the first model to outperform trained human chemists in predicting the outcomes of chemical reactions. Alpha then got Aaron, his former mathematics classmate and debate partner at Oxford to leave his job for the world of drug discovery.

We decided to focus on the one challenge that exists at almost every step: molecules need to be made. No matter how clever it looks on paper, a molecule is worthless unless it can be tested in a lab. The task of actually making molecules, known as chemical synthesis, is often a challenging problem, involving the combinatorial explosion of games like Go with moves that can’t be defined in a simple rulebook.

You start with a set of simple molecules which can be combined through chemical reactions (a ‘move’) to form more and more complex molecules, known as the ‘route’, until you arrive at your desired drug candidate. But how to combine these molecules? Trial and error is not an option, given the enormous cost of doing chemistry, and just enumerating all options to a client is unhelpful given that your average molecule can have hundreds of theoretically-possible routes. Searching this tree of routes and scoring the viability of such routes is where ML becomes very powerful.

We developed a machine-translation approach which takes in reactants and outputs the product of a reaction; an approach very similar to how Google Translate operates. This allows us to score the viability of each move. We combine this with fast tree search algorithms, used in models like AlphaGo to efficiently search the large combinatorial space of possible reactions.

To get this technology in front of users, we're building a cloud-based platform. Clients input the molecule they want to be made, our system designs a route for how to make it, and then the client can order this molecule through our platform. We don’t own a lab, but we partner with chemical manufacturers around the world who execute the routes we design. Combining automated chemical synthesis with compound ordering creates a better experience for the drug hunter who wants to focus on their science and just wants a vial with their compound without the cumbersome process of figuring out how to make it and where to get it from.

All that is what we were working on until the pandemic hit... and now we can answer the second part of the title: COVID Moonshot.

We had just finished YC W20 when a tweet from a team of scientists quickly changed our travel (and company) trajectory. A team of scientists at Diamond Light Source in the UK had shown that a selection of chemical fragments were effective at binding to a key part...