Structure-aware generative molecular design: from 2D or 3D?
TL;DR: I compared recent structure-explicit (3D) algorithms vs. structure-implicit (typically 2D) algorithms for structure-aware generative molecular design. I found that from a practical perspective structure-implicit (2D) methods are currently more useful, but, scoring function is important. See details and future outlook below, or jupyter notebook for data and analysis. Background Knowledge of protein structure and how a ligand binds provides critical insights enabling more explicit hypothesis of important intermolecular interactions, potentially new ligand growth vectors (or ones to avoid), or in the absence of any ligand it provides a model to test virtual molecules in silico. Therefore, structure-aware 3D generative molecular design (GMD) should capitalise in the same way, right? For this reason, 3D-GMD (algorithms that build the ligand in 3D) and structure-explicit generative molecular design (algorithms that utilise a representation of protein structure) are a large focus ...