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Chemical Predictions with 3 lines of code
State of the art results using Chemprop & graph neural networks
In this post, we use Machine Learning / AI to predict the properties of small molecules (a task known as QSAR). This is done by using state-of-the-art graph neural networks from the open-source library Chemprop.
Typical pharmaceuticals come in the form of small molecules that can regulate some biological processes in our bodies. Unfortunately, an unimaginable amount of things can go wrong in this process; the compounds can be toxic, clear very slowly from our bodies, interact with unintended other molecules, etc. We, therefore, want to very carefully be testing these small molecules before they ever get injected into anyone.
During the early phases of drug discovery, many different variations of small molecules are typically tested in lab-scale experiments for various properties, e.g., solubility, different forms of toxicity, binding affinities, etc. This process can be extraordinarily laborious, so wouldn’t it be great to use ML to predict these properties based on experiments already performed? This task, which is well known within cheminformatics, has received increasing attention in recent years due to advancements in deep learning.
