from pixels 2 molecules ⌬

Tutorial for building QSAR Models in KNIME: Part 1 In this blog post, I’ll guide you through the process of building QSAR (Quantitative Structure-Activity Relationship) models using the KNIME platform. Before starting, make sure you’ve installed the required cheminformatics nodes. If you need help with that, you can follow this video  or video for instructions. We will be using combination of native knime and cheminfomatics nodes. Once everything is set up, we can dive into preparing your bioactivity dataset and normalizing structures. Let’s get started! Preparing Your Bioactivity Dataset for Machine Learning In machine learning, high-quality data is crucial to achieving good model performance. The phrase " garbage in , garbage out " is particularly true here, so it’s essential to take the time to clean and normalize your dataset. For this tutorial, we are going to use the bioactivity dataset for carbonic anhydrase 9, with chembl ID:  CHEMBL3594   Here’s how you can prep...

Virtual screening is like searching for a needle in a haystack, where the needle represents a potential inhibitor within a vast chemical space. It serves as a guide, helping researchers navigate this ever-expanding chemical universe to find compounds with optimal drug-like properties, favorable pharmacokinetics, and similarity to known active compounds in both 2D and 3D. It’s akin to piloting a shuttle through nearby clusters of molecules that show promise for a target or a group of targets. The total chemical space is staggeringly vast—estimated to be on the order of 10^60 molecules if one only considers combinations of carbon, oxygen, nitrogen, and hydrogen. This immense size makes it impossible to explore entirely, but typical chemical libraries used in virtual screening contain about 10^6 compounds, allowing researchers to focus on the most promising regions of this space. However, even with millions of molecules available, these libraries still leave much of the chemical space une...