## 4 Discussion  
Accurately identifying druggable proteins is a prerequisite for drug development. In recent years, with the development of related sequencing technologies [42; 43], an increasing amount of protein sequence information has been obtained. Obtaining this information is much less costly than traditional laboratory methods. The large amount of protein sequence data makes it possible to identify druggable proteins through machine learning methods. In this context, we propose a druggable protein identification model based on pre-trained models and protein evolution information. We first used pre-trained models to extract the embeddings of proteins in the dataset and compared the extraction effects  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Acc** & **Sen** & **Spe** & **F-Score** & **MCC** \\ \hline DrugMiner & 0.9210 & 0.9280 & 0.9134 & 0.9241 & 0.8417 \\ GA-Bagging-SVM & 0.9378 & 0.9286 & 0.9445 & 0.9358 & 0.8781 \\ XGB-DrugPred & 0.9486 & 0.9375 & 0.9574 & 0.9417 & 0.8900 \\ DrugFinder & 0.9498 & 0.9633 & 0.9683 & 0.9460 & 0.8996 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with other druggable protein identification models.  
Figure 4: Model performance comparison on two different test sets.  
of three pre-trained models. Subsequently, we used Blast to calculate the PSSM of the sequence. Since the size of the PSSM is usually L * 20, which is not conducive to subsequent classification, we further processed the PSSM to obtain a 1200-dimensional feature. This feature was combined with the output of the pre-trained model. In order to eliminate the influence of redundant features on the accuracy of the classifier, we also used RF to perform feature selection on the combined features. Finally, we compared five commonly used machine learning classifiers and obtained the best result on the XGB classifier: 94.98% accuracy, 96.33% sensitivity and 96.83% specificity. Our model was also tested on an additional dataset of druggable proteins related to tumors, achieving an accuracy of 88.71% and precision of 93.72%. Although these results are slightly lower than those obtained on [PERSON]'s test set, they are still sufficient to demonstrate the model's generalization capability. From Table 4, it can be seen that compared with other models using the same dataset, this model has significantly improved the ability to identify druggable proteins. The proposed design and construction method of the druggable protein identification model in this paper also help with future drug development and related research fields.  
Conceptualization, M.Z. and F.W.; methodology, M.Z.; software, M.Z.; validation, M.Z. and T.L.; investigation, M.Z. and F.W.; resources, T.L.; data curation, M.Z.; writing--original draft preparation, M.Z.; writing--review and editing, T.L.; visualization, F.W.; supervision, T.L.; project administration, M.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used to support the findings of this study are freely available online at [[https://github.com/Melo-1017/DrugFinder](https://github.com/Melo-1017/DrugFinder)]([https://github.com/Melo-1017/DrugFinder](https://github.com/Melo-1017/DrugFinder)) (accessed on 19 May 2023).  
The authors declare no conflict of interest.