Diabetes mellitus (DM) is a chronic metabolic disorder characterized by elevated blood glucose levels, posing a significant global health burden. According to the World Health Organization (WHO) 2019 report, diabetes ranks among the top 10 leading causes of mortality, with an estimated 1.6 million deaths worldwide (World Health Organization: WHO, 2024; World Health Organization, 2020). In the United States, diabetes is a significant public health challenge, affecting approximately 38 million people (11.3% of the population) and leading to $327 billion in medical expenses and lost wages annually (CDC, 2024). Beyond economic costs, diabetes is associated with severe complications, including blindness, kidney failure, stroke, heart disease, and neuropathy. DM is broadly classified into type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), with T2DM accounting for over 90% of all cases. One decisive therapeutic target for managing Type 2 Diabetes Mellitus (T2DM) is the Dipeptidyl Peptidase-4 (DPP-4) enzyme, which plays a key role in regulating glucose metabolism. DPP-4 inhibitors, a class of FDA-approved medications, help control blood sugar levels by inhibiting this enzyme. However, current DPP-4 inhibitors have been linked to adverse effects such as gastrointestinal issues, severe joint pain, nasopharyngitis, hypersensitivity, and nausea (Huang et al., 2020). As a result, discovering safer and more effective DPP-4 inhibitors remains a critical research challenge.

Artificial intelligence (AI) has revolutionized diabetes management and drug discovery over the past two decades. Early AI models focused on predicting glucose levels, providing insulin dosage recommendations, and monitoring patients. In recent years, AI has rapidly advanced in the field of de novo drug design by leveraging large-scale molecular datasets. These models can not only generate novel drug candidates but also identify repurposable inhibitors and uncover complex relationships among genes, proteins, and disease mechanisms. In the field of DPP-4 inhibitor prediction, quantitative structure–activity relationship (QSAR) models have been widely developed using machine learning techniques such as random forest, support vector machines (SVMs), XGBoost, gradient boosting machines, and deep neural networks (DNNs) (Gong et al., 2021; Hermansyah et al., 2021; Ojo et al., 2021; Septiawan et al., 2022; Bustamam et al., 2021; Ajiboye et al., 2021). Although these models have demonstrated high predictive performance, they have limitations, including poor interpretability, data inefficiency, and a lack of reasoning capabilities. The black-box nature of deep learning models further complicates their use in critical healthcare applications, where transparency, logical reasoning, and explainability are vital (Hassan et al., 2022).

To address these challenges, neuro-symbolic (NeSy) AI (Hossain and Chen, 2025) has emerged as a promising paradigm that combines neural networks with symbolic reasoning for more interpretable and data-efficient learning. In contrast to traditional AI approaches that rely solely on data, Neuro-symbolic AI (NeSy AI) integrates domain knowledge with data-driven learning, enabling logical reasoning and making it especially well-suited for healthcare and drug discovery applications (Hossain et al., 2025) (Hassan et al., 2022). Studies identified numerous NeSy models that have demonstrated immense success in biomedical applications (Hossain and Chen, 2025; Yu et al., 2023; Wang W. et al., 2024), such as protein function prediction [MultiPredGO (Giri et al., 2020)], gene sequence analysis [KBANN (Towell and Shavlik, 1994)], diabetic retinopathy diagnosis [ExplainDR (Jang et al., 2021)], predicting the structure of proteins [extended KBANN (Maclin and Shavlik, 1994)], cardiotoxicity assessment hERG-LTN (Hossain et al., 2025), (Ontology) RRN (Yang et al., 2017), NSRL (Hohenecker and Lukasiewicz, 2020), Neuro-Fuzzy (Yang et al., 2020), FSKBANN (Kora et al., 2019), DeepMiRGO (Wang et al., 2019), NS-VQA (Yi et al., 2018), DFOL-VQA (Amizadeh et al., 2020), LNN (Riegel et al., 2020), NofM (Towell and Shavlik, 1991), PP-DKL (Lavin, 2022), FSD (Dobosz and Duch, 2008), CORGI (Arabshahi et al., 2021), NeurASP (Shi et al., 2019), XNMs (Teru et al., 2020), Semantic loss (Xu et al., 2018), NS-CL (Mao et al., 2019), and Logic Tensor Networks (LTNs) (Badreddine et al., 2021). In this study, we explore a hybrid neuro-symbolic approach integrating LTNs for DPP-4 bioactivity prediction, aiming to enhance predictive accuracy while enabling logical reasoning for novel drug discovery in T2DM treatment.

The paper’s main contributions are summarized as follows: 1) We developed a scalable and robust AI predictive model that demonstrates significant improvements in accuracy for predicting the potency of T2DM inhibitors. 2) A novel integration of data and rules (Knowledge) for DPP-4 inhibitor bioactivity classification. 3) We acquired and utilized a more diverse set of compound datasets, including chemical embeddings, descriptors, fingerprints, and physicochemical properties, which previous studies have not explored. The developed NeSyDPP-4 model can be used to discover novel DPP-4 active drugs by scanning large molecular datasets, such as ZINC, and identifying new candidate compounds, thereby accelerating the de novo design of drugs. Additionally, it facilitates QSAR model downstream applications, including virtual screening, contraindications, bioactivity indications, and other key elements of DPP-4 inhibitor therapy in clinical settings. These applications encompass docking, affinity prediction, ADMET analysis, and molecular dynamics (MD) studies for DPP-4 clinical applications.

The remainder of this manuscript is organized as follows: Section 2 provides the background, offering essential insights into the problem domain and its significance. Section 3 briefly reviews related work, highlighting existing approaches and their contributions to DPP-4 bioactivity classification. Section 4 describes the methodology, detailing the proposed approach, datasets, and algorithms used. Section 5 reports the results obtained from the experimental evaluation. Section 6 offers a discussion, interpreting and comparing the findings with existing studies. Finally, Section 7 concludes the paper with key findings and guidance for future research.

DPP-4 inhibitors are a significant class of compounds used in treating type 2 diabetes; predicting their bioactivity is essential in early drug discovery. Several machine learning-based studies have been explored for classifying DPP-4 inhibitors. For instance, QSAR models have been widely applied with descriptors such as molecular fingerprints and physicochemical properties to predict inhibitory activity. Techniques like random forests, support vector machines, and deep neural networks have shown promising results in improving classification accuracy and guiding virtual screening processes. For instance, Hermansyah et al. (2020) employed Random Forest and DNNs, achieving an accuracy of 0.9221 using CDK fingerprint and molecular properties. Bustamam et al. (2021) developed a QSAR–DNN model, yielding an accuracy of 0.904. In addition, Cai et al. (2017) applied a Naïve Bayesian (NB) approach using various fingerprint extractions, such as ECFP_4, ECFP_6, FCFP_4, and FCFP_6, reaching an accuracy of 0.872. Ulfa et al. (2021) combined Conv1D and LSTM layers for bioactivity classification, using CatBoost-selected fingerprint features, and achieved an accuracy of 0.8618. Finally, Hermansyah et al. (2023) used XGBoost with CDK and ECFP-6 features, reporting an accuracy of 0.8164. These studies highlight the progression from traditional machine learning models to advanced deep learning approaches, which have steadily improved predictive performance in DPP-4 bioactivity classification. However, to date, neuro-symbolic approaches have not been effectively integrated into DPP-4 research, even though such integration is essential for the discovery of novel chemical compounds. Moreover, previous research lacked experimentation with diverse chemical features, such as chemical language model embeddings and physicochemical properties. To address these gaps, we propose the NeSyDPP-4 strategy, which leverages Logic Tensor Networks and incorporates a wide range of feature representations, including LLaMA3.2 embeddings, PaDELPy CDKExtended fingerprints, Morgan fingerprints, chemical language model embeddings, and physicochemical properties.

This section describes a set of procedures to determine the performance of LTNs (Badreddine et al., 2021), DNNs, and an advanced language model known as Transformer, using the ChEMBL BindingDB, PubChem, and GTP datasets related to DPP-4 inhibitors. This section covers the entire pipeline, including the material procurement, data preprocessing, feature extraction, simulation environment, network architecture, LTN knowledge-based setting, the training and inferencing phases, and the evaluation metrics used to measure the trained model’s performance.

This section outlines the performance of the proposed NeSyDPP-4 model in identifying potential DPP-4 inhibitors by integrating logical rules into a neural network via the LTN architecture. To achieve this, we extracted a diverse set of molecular features from each SMILES/drug representation. These include the Morgan fingerprint, CDK extended descriptors, and embeddings generated from chemical language models such as ChemBERTa-2 and LLaMA3.2 (via the Hugging Face platform). Additionally, physicochemical properties were computed using RDKit to enrich the feature space with interpretable molecular characteristics. This section presents four result grids: Table 3 shows all the features separated and combined as input results for an ablation study; Table 4 exposes the fair comparison with baseline DNN and transformer architecture performance; Table 5 summarizes the model’s performance on the external evaluation; and finally, Table 6 presents the benchmarking evaluation. To illustrate, Table 3 depicts the different input performances of NeSyDPP (LTN). The best-performing feature set is combining CDKExtended + ECFP, which yielded the highest accuracy (97.25%), F1-score (97.23%), AUC ROC (97.19%), and MCC (94.46%), while physicochemical features alone yield the lowest performance of accuracy (73.49%), F1-score (73.16%), AUC ROC (73.09%), and MCC (46.38%). ChemBERTa-2 and LLaMA3.2 performed comparably but achieved lower performance than the fingerprint-based methods. Overall, physicochemical properties alone are insufficient for effective bioactivity classification.

In addition, Table 4 exhibits the internal validation results comparing the performance of NeSyDPP-4 (LTN), DNN, and Transformer models. The NeSyDPP-4 model, using CDKExtended and ECFP features, achieved the highest performance with 97.25% accuracy and an MCC of 94.46%, highlighting the strength of neuro-symbolic reasoning. The DNN model, using the same input features but without reasoning capabilities, yielded slightly lower results (96.95% accuracy and 93.85% MCC). In contrast, the Transformer model, which relied on SMILES embeddings, exhibited the weakest performance (78.21% accuracy and 56.41% MCC). These findings accentuate the effectiveness of fingerprint-based features over SMILES-based language models for bioactivity classification tasks. Furthermore, in the external evaluation, using the DTC dataset (Table 5), NeSyDPP-4 continued to outperform baseline models, confirming its strong generalization capability.

Finally, Table 6 presents a state-of-the-art (SOTA) performance comparison. The proposed NeSyDPP-4 model outperforms all baseline models across accuracy metrics. Traditional models such as Random Forest and XGBoost achieved 92.21% and 81.64% accuracy, respectively, while deep learning models like DNN and QSAR –DNN reached approximately 90%, as reported in prior benchmarks. These results underscore the superior performance of the neuro-symbolic NeSyDPP-4 model. In addition, Figure 4 illustrates the training and validation accuracy curves over 100 epochs for both the DNN and NeSyDPP-4 simulations.

This article aimed to develop a neuro-symbolic model leveraging LTN, an integration of data and a logic-driven approach, for predicting DPP-4 inhibition in diabetes mellitus. One of the key challenges in DPP-4 inhibitor research and AI integration is the absence of unified, data and knowledge-driven experimental frameworks that support logical reasoning. Moreover, current approaches fail to effectively utilize diverse features and integrate logical rules, such as extracting embeddings from large language models (LLMs) and physicochemical properties, to predict DPP-4 bioactivity accurately. Furthermore, prior studies have focused on data-driven approaches such as DNN and Transformer. In this context, our study introduces LTN-based NeSyDPP-4, a neuro-symbolic classifier trained on the curated diverse bioactivity activity data and logical rules (Table 1), which demonstrates superior performance in predicting DPP-4 inhibitory activity compared to existing models. Notably, some studies suggest that it can be semi-interpretable since rules (Table 1) are apparently understandable by humans regarding how models should make decisions. However, the study’s findings provide valuable insights into the applicability and robustness of the LTN model, discovering bioactivity behavior. To illustrate, the utilization of this advanced machine learning technique (LTN) surpassed the state-of-the-art performance compared to other models with classification tasks; the proposed model demonstrates superior accuracy of 0.9725 and an MCC score of 0.9446 in the internal dataset for DPP-4 inhibitor bioactivity prediction. In contrast, several other studies have reported comparable results: the QSAR-DNN model by Bustamam et al. (2021) achieved an accuracy of 0.9040; Ulfa et al. (2021) reported an accuracy of 0.8618 using Conv1D–LSTM; random forest by Hermansyah et al. (2020) yielded an accuracy of 0.9221; and DNN by Hamzah et al. (2020) obtained an accuracy of 0.9060. Furthermore, the NB model by Cai et al. (2017) gained an accuracy of 0.8720, while ML-based XGBoost by Hermansyah et al. (2023) reported an accuracy of 0.8164.

Overall, this study emphasizes the value of integrating neuro-symbolic modeling, which combines data-driven learning with logical rule-based reasoning, for highly accurate classification of DPP-4 inhibitor activity in the context of diabetes mellitus, a task that conventional data-driven AI approaches (DNN, Transformer) are incapable of. Significantly, the developed NeSyDPP4 model holds substantial promise for future applications, including the discovery of novel DPP-4 compounds, prediction of DPP-4-related pharmacological interactions, efficient high-throughput screening of molecular libraries to identify potential active agents, and conducting virtual screening based on identified approved/existing substance compounds that can be utilized as drug re-purposing (Figures 5B, C).

Diabetes mellitus is a vital global health concern, and discovering effective chemical compounds is decisive to tackling this epidemic. In this study, we develop a QSAR system to identify a therapeutic potential compound of DPP-4 inhibitors using an advanced AI framework called LTN that integrates domain-specific knowledge into neural networks. The study is a pioneer in applying the neuro-symbolic strategy in the DM domain and provides new insights that reveal a higher performance for DPP-4 bioactivity classification. The root cause of achieving such performance could be upholding learning and reasoning principles and training neural networks with rules. Furthermore, we experimented with DNN, an NLP Transformer model, whereas the LTN-based model NeSyDPP-4–QSAR attained the highest accuracy of those baselines’ approaches and prior SOTA strategies. In conclusion, the findings of this study prove that the neuro-symbolic approach for uncovering potential DPP-4 inhibitors is promising. However, an ideal direction for future work could involve integrating additional potential neuro-symbolic strategies, such as Semantic Loss and DeepProbLog, to study GLP-1, IDO, and PTP1B inhibitors, which would include extracting a variety of new descriptors and fingerprints from different datasets (PubChem and Drug Bank), focusing on regression tasks.