6 Case Study: Small Molecule Binding to an RNA Target
This chapter covers
- An exemplary quantitative structure-activity relationship (QSAR) pipeline for understanding small molecule binding to RNA targets
- Advanced molecular representation and descriptor calculation methods, especially in low data availability contexts
- Representative data splitting with the Kennard-stone algorithm and dimensionality reduction algorithms like principal component analysis (PCA)
- Sequential ensemble learning with gradient boosting
- Advanced methods for model-specific and model-agnostic interpretability
In drug discovery, attenuating RNA (ribonucleic acid) targets with small molecules has emerged as a promising strategy to develop novel therapeutics. RNA is recognized as a versatile molecule that plays key regulatory roles in cells by carrying genetic information. The binding of a small molecule to an RNA target can lead to a variety of outcomes, such as inhibiting a specific RNA-protein interaction, altering RNA splicing patterns, or promoting RNA degradation. For example, small molecules that modulate RNA structures involved in cancer progression have been investigated as potential anticancer agents.