ProLinker-Generator: Design of a PROTAC Linker Base on a Generation Model Using Transfer and Reinforcement Learning  
[PERSON]  
1 College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China;  
[EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (D.S.); [EMAIL_ADDRESS] (C.Z.)  
[PERSON]  
1 College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China;  
[EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (D.S.); [EMAIL_ADDRESS] (C.Z.)  
[PERSON]  
1 College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China;  
[EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (D.S.); [EMAIL_ADDRESS] (C.Z.)  
An Su  
efficiency and stability of the target protein-PROTAC-E3 ligase ternary complex formation, making it a key factor in determining degradation activity [1; 12; 13]. As a critical \"molecular bridge\" in PROTAC, the length, conformation, and chemical composition of the linker play a decisive role in the stability of the ternary complex and degradation efficiency. An ideal linker must balance steric hindrance with synergistic effect loss [14]. In addressing these challenges, artificial intelligence (AI) has become a pivotal tool for accelerating the rational design of PROTAC linkers, showcasing substantial potential in optimizing linker properties.  
In recent years, AI has achieved remarkable progress in accelerating de novo molecular design [15; 16; 17; 18; 19]. [PERSON] et al. trained a variational autoencoder (VAE) model to encode part of the ZINC database into latent vectors and map them into a hidden space, enabling the generation of new molecules through sampling and decoding [20; 21; 22]. [PERSON] et al. introduced the PROTAC-DB database [23]; [PERSON] et al. applied transformer models for de novo PROTAC design [16]; and [PERSON] et al. developed the DeepPROTACs model to predict PROTAC degradation capability, achieving an average prediction accuracy of 77.95% on test sets [24]. These breakthroughs demonstrate that AI technology holds promise for overcoming the limitations of traditional linker design, particularly in aspects such as property optimization.  
Notably, the GPT (Generative Pre-trained Transformer) architecture has been increasingly applied in molecular design, demonstrating strong capabilities in generating diverse and chemically valid molecules, including MolGPT [25], 3 DSMILES-GPT [26], and RM-GPT [27]. In parallel, reinforcement learning (RL) has become a key tool for controllable molecular generation, enabling models to optimize structures toward desired properties through reward-guided training [28; 29; 30]. Building upon these advancements, our study introduces ProLinker-GENERATOR, which integrates GPT with RL strategies to enable targeted PROTAC linker design with enhanced structural diversity and improved property control.  
In this study, we developed the ProLinker-GENERATOR, a GPT-based method designed to generate novel PROTAC linkers. The model was first pre-trained on large-scale public molecular datasets to build a comprehensive foundation of chemical knowledge. It was then fine-tuned on a specialized linker dataset to enhance its applicability in linker design. During the generation phase, the model is guided by predefined molecular scaffolds and employs RL strategies to optimize and control molecular properties. Additionally, through systematic investigation, this study reveals how different pre-training datasets influence the quality and diversity of the generated linkers. In addition, the effects of using different molecular representations (SMILES and SELFIES) and using data augmentation methods on linker design are discussed, providing key theoretical support for the rational design of PROTAC linkers.