Investigating 3D Atomic Environments for Enhanced QSAR
This work addresses the problem of incorporating 3D molecular shape into QSAR for drug design, offering an incremental improvement by integrating SOAP descriptors into existing frameworks.
The paper tackled the challenge of predicting molecular bioactivity by introducing a 3D QSAR method using Smooth Overlap of Atomic Positions (SOAP) to capture molecular shape, achieving competitive performance with state-of-the-art methods on pIC50 ligand-binding prediction in random and scaffold splits.
Predicting bioactivity and physical properties of molecules is a longstanding challenge in drug design. Most approaches use molecular descriptors based on a 2D representation of molecules as a graph of atoms and bonds, abstracting away the molecular shape. A difficulty in accounting for 3D shape is in designing molecular descriptors can precisely capture molecular shape while remaining invariant to rotations/translations. We describe a novel alignment-free 3D QSAR method using Smooth Overlap of Atomic Positions (SOAP), a well-established formalism developed for interpolating potential energy surfaces. We show that this approach rigorously describes local 3D atomic environments to compare molecular shapes in a principled manner. This method performs competitively with traditional fingerprint-based approaches as well as state-of-the-art graph neural networks on pIC$_{50}$ ligand-binding prediction in both random and scaffold split scenarios. We illustrate the utility of SOAP descriptors by showing that its inclusion in ensembling diverse representations statistically improves performance, demonstrating that incorporating 3D atomic environments could lead to enhanced QSAR for cheminformatics.