# Data-Driven Design of PROTAC Linkers to Improve PROTAC Cell Membrane Permeability

## TL;DR

This paper introduces a new method to design PROTAC linkers that improve cell membrane permeability using machine learning and reinforcement learning.

## Contribution

The novel contribution is a generative model, PROTAC-TS, that explicitly optimizes PROTAC linkers for cell membrane permeability.

## Key findings

- A cell membrane permeability prediction model achieved high accuracy (R² = 0.710).
- PROTAC-TS successfully generated linkers with high predicted permeability and PROTAC likeness.

## Abstract

Proteolysis-targeting
chimeras (PROTACs) are promising next-generation
therapeutics for the degradation of disease-associated proteins. However,
optimizing the physicochemical properties of PROTACs, particularly
their poor cell membrane permeability, remains challenging. Traditionally,
PROTAC linkers have been manually designed to improve cell membrane
permeability. Although recent machine learning-based approaches have
enabled the rational design of PROTAC linkers, no linker design methods
that explicitly address cell membrane permeability have been reported.
In this study, we developed PROTAC-TS, a linker generative model that
combines a chemical language model and reinforcement learning to control
cell membrane permeability. We first constructed a prediction model
of cell membrane permeability, which achieved high prediction performance
(R
2 = 0.710). By integrating this prediction
model into the generative model, we successfully designed linkers
of PROTACs with high predicted cell membrane permeability while considering
PROTAC likeness. Our results highlight the potential of PROTAC-TS
in accelerating PROTAC development with favorable cell membrane permeability.