Reinforcement Learning-Driven Linker Design via Fast Attention-based Point Cloud Alignment

Rebecca Manuela Neeser, Mehmet Akdel, Daniel Kovtun, Luca Naef

Keywords: Machine Learning, ICML, Reinforcement Learning, Drug Discovery, PROTACs, Shape Alignment, Drug Design, Linker Design, Generative Models

Abstract: PROteolysis-TArgeting Chimeras (PROTACs), which are comprised of two protein-binding domains connected via a linker, are a novel class of small molecules that enable the degradation of disease-relevant proteins. The design and optimization of the linker portion 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 that 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, which allows capturing a desired geometry to link the anchor and warhead. This method successfully generates linkers that satisfy 2D and 3D requirements, achieving state-of-the-art results in linker design for more efficient PROTAC optimization.

Submission Number: 26