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TRL-PROTAC: A pre-trained generator of PROTACs targeting specific proteins optimized by reinforcement learning

Yuhao Dai¹ and Fei Zhu¹

1. School of Computer Science and Technology, Soochow University
   215006 Suzhou, Jiangsu, China
   20224227033@stu.suda.edu.cn; zhufei@suda.edu.cn

Abstract

Proteolysis-targeting chimeras (PROTACs) introduce a novel paradigm in drug development, incorporating three essential components: the warhead, the E3 ligand, and the linker. The complexity of the ternary structure, particularly the intricate design of the linker, presents a significant challenge in PROTACs drug design. Here an integrated protocol for design and evaluation of PROTACs targeting specific proteins, called TRL-PROTAC is proposed. TRL-PROTAC is focused on the de novo design of complete PROTACs by effectively joining the designed ligands targeting the proteins of interest (POI) with linkers. The ligands for POIs and E3 ligases are generated by a molecular generation model for targeting proteins, and the linker design is generated by a sequence-to-sequence model consisting of a transformer variant and the policy-based reinforcement learning method which is employed to optimize the reward values for generating PROTACs. The three components are then integrated and optimized based on their pharmacokinetic (PK) and degradation (DEG) properties. The experimental results have strongly confirmed that TRL-PROTAC is superior in optimizing properties. For existing PROTACs, TRL-PROTAC improves DEG scores by 0.45 and lowers PK scores by 1.20. Moreover, TRL-PROTAC enhances binding affinity by 2.15 in PROTACs generated from scratch.

Key words

proteolysis-targeting chimeras, transformer, reinforcement learning, drug design, protein-ligand interaction

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS240327039D

Publication information

Volume 21, Issue 4 (September 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Dai, Y., Zhu, F.: TRL-PROTAC: A pre-trained generator of PROTACs targeting specific proteins optimized by reinforcement learning. Computer Science and Information Systems, Vol. 21, No. 4, 1293–1320. (2024), https://doi.org/10.2298/CSIS240327039D