Drug discovery is lengthy process which costs 2.6B per approved drug on average. The cost is so high because finding a small molecule that binds to a particular target is a perilous highly uncertain process that can’t be completed in a decade in many of the cases. And even when a molecule is found it might not be doing its job very well and might very likely fail in later stages of trials. Our goal is to find much better drug candidate molecules and do it very fast.
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The number of possible drug-like molecules in the Universe is comparable to the number of atoms in the Universe.
All of the cures found in the history of mankind come from sampling a tiny fraction of this space. The rest of this space likely contains cures for most of the remaining diseases if knew how to search it better.
Our goal is to develop a search technique that could reach the exascale, or 10^18 molecules.
The search in this scale would take 1000 Billion CPU years for existing physics-based methods to simulate.
We define molecules as a set of standard building blocks (think of LEGO bricks) that could be recombined together in various ways. We define search in the molecular space as search in the space of possible configurations of these blocks. Similar to how AlphaZero which uses Monte Carlo Tree Search (MCTS) to master the game of Go, our algorithm called LambdaZero uses MCTS to navigate the space of molecules.
Dopamine receptor D4 is an important protein in the human brain. Searching in the larger space of molecules* results in finding molecules with more negative binding energy. More negative binding energy means stronger association between the small molecule and the protein and therefore better binding and better properties as a drug.
*Ultra-large Scale Docking for Discovering New Chemotypes, Lyu at al. Nature, 2019.