Small molecule drugs are chemicals that interact with cellular protein machinery and affect the functions of this machinery in some way. Often, drugs are meant to inhibit the activity of single protein targets, and those targets are thought to be involved in a disease process. A classic approach to identify such candidate molecules is to physically make them, one by one, and then expose them to the protein target of interest and test if the two interact. This can be a fairly laborious and time-intensive process.
The US Food and Drug Administration (FDA) has approved roughly 2,000 novel molecular entities in its entire history. However, the number of chemicals in druglike space has been estimated to be 10^60, a space far too big to physically search. There are likely effective treatments for human ailments hiding in that chemical space, and better methods to find such treatments are desirable to us all.
To evaluate potential search methods in small molecule chemistry, competition host Leash Biosciences physically tested some 133M small molecules for their ability to interact with one of three protein targets using DNA-encoded chemical library (DEL) technology.
Recent advances in ML approaches suggest it might be possible to search chemical space by inference using well-trained computational models rather than running laboratory experiments. Similar progress in other fields suggest using ML to search across vast spaces could be a generalizable approach applicable to many domains.
