Deep Generative Models for 3D Linker Design

Rational compound design remains
a challenging problem for both
computational methods and medicinal chemists. Computational generative
methods have begun to show promising results for the design problem.
However, they have not yet used the power of three-dimensional (3D)
structural information. We have developed a novel graph-based deep
generative model that combines state-of-the-art machine learning techniques
with structural knowledge. Our method (“DeLinker”) takes
two fragments or partial structures and designs a molecule incorporating
both. The generation process is protein-context-dependent, utilizing
the relative distance and orientation between the partial structures.
This 3D information is vital to successful compound design, and we
demonstrate its impact on the generation process and the limitations
of omitting such information. In a large-scale evaluation, DeLinker
designed 60% more molecules with high 3D similarity to the original
molecule than a database baseline. When considering the more relevant
problem of longer linkers with at least five atoms, the outperformance
increased to 200%. We demonstrate the effectiveness and applicability
of this approach on a diverse range of design problems: fragment linking,
scaffold hopping, and proteolysis targeting chimera (PROTAC) design.
As far as we are aware, this is the first molecular generative model
to incorporate 3D structural information directly in the design process.
The code is available at https://github.com/oxpig/DeLinker.