Van Thuy Hoang, O-Joun Lee
CaMol, a context-aware graph causality inference framework, improves few-shot molecular property prediction by leveraging causal substructures and chemical knowledge.
Predicting the properties of molecules is important for applications like drug discovery and protein structure prediction. However, it can be challenging when only a few examples are available. The new framework, CaMol, helps address this by using a method called causal inference, which focuses on understanding the cause-and-effect relationships within molecules. It identifies important parts of molecules that are directly linked to their properties, making predictions more accurate and efficient. This approach not only improves prediction accuracy but also aligns well with existing chemical knowledge, making it easier to interpret the results.
