## 3 Results
### Molecular Generation Based on Reinforcement Learning  
Computational properties, such as LogP, QED, and SAS, can guide the generation of PROTAC linkers. Firstly, the linker length can affect the formation of PROTACs. In the study by [PERSON] et al., PROTACs were tested for their ability to degrade endogenous ER-\(\alpha\) in MCF7 breast cancer cells. They explored whether the position at which the linker was attached to the pentapeptide was significant [46]. Ultimately, it was found that PROTACs with linker atomic lengths of less than 16 atoms were preferred [47]. Moreover, according to the study by [PERSON] et al., the LogP value of the linker should typically be maintained within a reasonable range to balance hydrophilicity and lipophilicity, thereby avoiding issues such as molecular aggregation or reduced solubility due to excessive hydrophobicity [48]. High LogP may lead to aggregation or poor membrane permeability, while low LogP may reduce cellular uptake efficiency [46]. In this section, RL [28] was used to regulate linker generation by controlling linker length and LogP values (Figure 3). RL provides guidance to the model through its unique reward-and-penalty mechanism, directing it to generate linkers with desirable properties. Firstly, by constraining the linker's LogP to the range of 1 \(<\) LogP \(<\) 3, a clear shift in the lipophilicity distribution of the RL-generated molecules can be observed (Figure 3A). Secondly, by limiting the linker length to fewer than 20 atoms, the length distribution of the generated molecules remains largely within the desired range (Figure 3B). When both strategies are applied simultaneously, similar improvements are observed (Figure 3C). These results collectively demonstrate that reinforcement learning can effectively guide the model to generate linkers with targeted and desirable properties. Figure 2: Workflow of the molecular generator, including pre-training, fine-tuning, reinforcement learning, and evaluation. We further evaluated the impact of RL on the evaluation metrics for molecular generation (Table 1). The results indicated that the application of the reward-and-punishment mechanism improved the validity of the generated linkers. Meanwhile, the novelty of the molecules remained largely unchanged, suggesting that the model's ability to explore novel structures was not affected. However, due to the targeted constraints imposed by RL, the available chemical space was somewhat restricted, leading to a decrease in the uniqueness and KL divergence of the generated molecules. This phenomenon is reasonable and is expected when optimization mechanisms are introduced to refine specific properties. This study employs a three-stage training strategy--pre-training, transfer learning, and reinforcement learning--to develop a GPT model capable of generating efficient and optimized PROTAC linkers. Initially, pre-training on a large molecular database ensures the consistency of SMILES representations. Although the pre-trained dataset from ChEMBL  
Figure 3: Linker generation with RL controlling (**A**) 1 \(<\) LogP \(<\) 3, (**B**) SMILES_Length \(<\) 20, (**C**) and 1 \(<\) LogP \(<\) 3 and SMILES_Length \(<\) 20 at the same time. contains drug-like molecules, it does not fully meet the specific structural and property requirements for PROTAC linkers. The resulting molecules exhibit low SAS, indicating ease of synthesis; however, their low quantitative estimates of QED values and overly complex molecular structures render them unsuitable as linkers. After fine-tuning the model via transfer learning, the generated molecules, as shown in Figure 4C, demonstrate higher structural similarity to those in PROTAC-DB (Figure 4A). Additionally, these molecules exhibit higher QED values and improved compatibility, although their LogP values remain suboptimal. Specifically, the LogP values of the generated molecules are predominantly distributed between 0 and 1, rarely satisfying the target range from 1 to 3.