How Computation Predictions Empower Drug Discovery
Posted September 27, 2021
by Tanuja Koppal, Ph.D.
Q: Can you share with us the goals of the ATOM consortium?
A: The vision of the ATOM research initiative is to use ML and AI to bring together data from public databases and from pharmaceutical partners to perform multi-parameter optimization on a drug target. Another aspect of the ATOM pipeline is to do automated experimentation. Nearly five years ago, the pharmaceutical company GlaxoSmithKline (GSK) and the national laboratories (Lawrence Livermore, Oak Ridge, Argonne, and Brookhaven) started re-envisioning drug discovery as a computationally driven approach. They realized that if we are going to do personalized medicine for a patient, we need to do it much faster, with fewer resources and a higher success rate. That’s where the idea of ATOM and using computational tools along with rapid experimental drug discovery came from….
Q: How do the experimental and computational components work together?
A: There are two kinds of computational models. Parameter-level models measure and predict experimental endpoints such as hERG channel activity, MDCK permeability, and more. There is a lot of data around those parameters that can be used to develop AI/ML models. The long-term goal, however, is to use systems level computation, where models can predict a “therapeutic index,” i.e., how safe and effective a drug is based on its on-target activity and toxicity, at predicted in vivo concentrations of the drug. What we can do right now is parameter level modeling and some amount of systems level modeling for pharmacokinetics. However, in the future we are looking to do mostly systems level modeling. We are also using transfer learning or matrix learning approaches to see how little data you need to understand a target based on what you already know about a related target.
“Human biology is very complex and drug discovery is a hard problem to tackle.”
There are two reasons why we do experiments alongside computation. One is to make and test compounds to validate predictions and then use the compounds in “real” biology….
