Abstract
Obtaining the desired effect of drugs is highly dependent on their molecular geometries. Thus, the current prevailing paradigm focuses on 3D point-cloud atom representations, utilizing graph neural network (GNN) parametrizations, with rotational symmetries baked in via E(3) invariant layers. We prove that such models must necessarily disregard chirality, a geometric property of the molecules that cannot be superimposed on their mirror image by rotation and translation. Chi rality plays a key role in determining drug safety and potency. To address this glaring issue, we introduce a novel field-based representation, proposing refer ence rotations that replace rotational symmetry constraints. The proposed model captures all molecular geometries including chirality, while still achieving highly competitive performance with E(3)-based methods across standard benchmarking metrics.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
"E(3)-equivariant Models Cannot Learn Chirality Field-based Molecular Generation", Technical Disclosure Commons, (April 28, 2025)
https://www.tdcommons.org/dpubs_series/8042