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

Yuhao Dai ; Fei Zhu

Computer Science and Information Systems, Tome 21 (2024) no. 4

Résumé

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.

Détail

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