Reinforcement Learning-Driven Linker Design via Fast Attention-based
  Point Cloud Alignment
Rebecca M. Neeser Mehmet Akdel Daniel Kovtun Luca Naef
Proteolysis-Targeting Chimeras (PROTACs) represent a novel class of small
molecules which are designed to act as a bridge between an E3 ligase and a
disease-relevant protein, thereby promoting its subsequent degradation. PROTACs
are composed of two protein binding "active" domains, linked by a "linker"
domain. The design of the linker domain is challenging due to geometric and
chemical constraints given by its interactions, and the need to maximize
drug-likeness. To tackle these challenges, we introduce ShapeLinker, a method
for de novo design of linkers. It performs fragment-linking using reinforcement
learning on an autoregressive SMILES generator. The method optimizes for a
composite score combining relevant physicochemical properties and a novel,
attention-based point cloud alignment score. This new method successfully
generates linkers that satisfy both relevant 2D and 3D requirements, and
achieves state-of-the-art results in producing novel linkers assuming a target
linker conformation. This allows for more rational and efficient PROTAC design
and optimization. Code and data are available at
https://github.com/aivant/ShapeLinker.