3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang
Deep learning has achieved tremendous success in designing novel chemical
compounds with desirable pharmaceutical properties. In this work, we focus on a
new type of drug design problem -- generating a small "linker" to physically
attach two independent molecules with their distinct functions. The main
computational challenges include: 1) the generation of linkers is conditional
on the two given molecules, in contrast to generating full molecules from
scratch in previous works; 2) linkers heavily depend on the anchor atoms of the
two molecules to be connected, which are not known beforehand; 3) 3D structures
and orientations of the molecules need to be considered to avoid atom clashes,
for which equivariance to E(3) group are necessary. To address these problems,
we propose a conditional generative model, named 3DLinker, which is able to
predict anchor atoms and jointly generate linker graphs and their 3D structures
based on an E(3) equivariant graph variational autoencoder. So far as we know,
there are no previous models that could achieve this task. We compare our model
with multiple conditional generative models modified from other molecular
design tasks and find that our model has a significantly higher rate in
recovering molecular graphs, and more importantly, accurately predicting the 3D
coordinates of all the atoms.