Mengmeng Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Mengmeng Liu has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Michal Brylinski
Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA
3 shared publications - Gopal Srivastava
Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
2 shared publications - Jagannathan Ramanujam
Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
2 shared publications - Xialong Ni
Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
1 shared publication
Latest Publications
- Open Access
- 18 July 2026
- Published by MDPI AG in Biomolecules
- Vol. 16 (7) , 1049
AUKAT: Conditional VAE-Driven Augmentation and Neural Modeling of Enzyme Turnover Numbers
Accurate prediction of enzyme turnover numbers () is essential for applications in systems biology, metabolic engineering, and drug discovery, yet remains challenging due to the limited availability and uneven distribution of experimental data. Here, we present AUKAT, an integrated framework that combines conditional generative modeling with deep neural prediction to improve estimation. A conditional variational autoencoder generates synthetic training instances in embedding space, followed by a selection pipeline that retains samples with strong agreement across independent evaluators, thereby ensuring data reliability. A hybrid convolutional neural network and transformer-based architecture is then used to predict from substrate, enzyme functional, and species embeddings. Incorporating synthetic data improved predictive performance for both random forest and neural network models in five-fold cross-validation, with larger gains observed for the neural network architecture. Benchmarking against DLKcat demonstrated comparable predictive accuracy on the standard test set, while evaluation on stricter unseen subsets indicated improved generalization for low-similarity substrates and enzymes. Feature importance analysis further showed that AUKAT leverages substrate, enzyme functional, and species information in a more balanced manner rather than relying predominantly on a single feature source. In addition, AUKAT-human, a specialized model trained using a pre-training and fine-tuning strategy, achieved improved prediction accuracy for human enzyme kinetics. Overall, AUKAT provides a scalable approach for enzyme kinetics prediction and offers a practical solution to data scarcity in biochemical modeling.- 18 July 2026
- Published by MDPI AG in Biomolecules
- Vol. 16 (7) , 1049
Open AccessAUKAT: Conditional VAE-Driven Augmentation and Neural Modeling of Enzyme Turnover Numbers
Accurate prediction of enzyme turnover numbers () is essential for applications in systems biology, metabolic engineering, and drug discovery, yet remains challenging due to the limited availability and uneven distribution of experimental data. Here, we present AUKAT, an integrated framework that combines conditional generative modeling with deep neural prediction to improve estimation. A conditional variational autoencoder generates synthetic training instances in embedding space, followed by a selection pipeline that retains samples with strong agreement across independent evaluators, thereby ensuring data reliability. A hybrid convolutional neural network and transformer-based architecture is then used to predict from substrate, enzyme functional, and species embeddings. Incorporating synthetic data improved predictive performance for both random forest and neural network models in five-fold cross-validation, with larger gains observed for the neural network architecture. Benchmarking against DLKcat demonstrated comparable predictive accuracy on the standard test set, while evaluation on stricter unseen subsets indicated improved generalization for low-similarity substrates and enzymes. Feature importance analysis further showed that AUKAT leverages substrate, enzyme functional, and species information in a more balanced manner rather than relying predominantly on a single feature source. In addition, AUKAT-human, a specialized model trained using a pre-training and fine-tuning strategy, achieved improved prediction accuracy for human enzyme kinetics. Overall, AUKAT provides a scalable approach for enzyme kinetics prediction and offers a practical solution to data scarcity in biochemical modeling. - Open Access
- 29 July 2024
- Published by MDPI AG in Machine Learning and Knowledge Extraction
- Vol. 6 (3) , 1782-1797
Insights from Augmented Data Integration and Strong Regularization in Drug Synergy Prediction with SynerGNet
SynerGNet is a novel approach to predicting drug synergy against cancer cell lines. In this study, we discuss in detail the construction process of SynerGNet, emphasizing its comprehensive design tailored to handle complex data patterns. Additionally, we investigate a counterintuitive phenomenon when integrating more augmented data into the training set results in an increase in testing loss alongside improved predictive accuracy. This sheds light on the nuanced dynamics of model learning. Further, we demonstrate the effectiveness of strong regularization techniques in mitigating overfitting, ensuring the robustness and generalization ability of SynerGNet. Finally, the continuous performance enhancements achieved through the integration of augmented data are highlighted. By gradually increasing the amount of augmented data in the training set, we observe substantial improvements in model performance. For instance, compared to models trained exclusively on the original data, the integration of the augmented data can lead to a 5.5% increase in the balanced accuracy and a 7.8% decrease in the false positive rate. Through rigorous benchmarks and analyses, our study contributes valuable insights into the development and optimization of predictive models in biomedical research.
- 29 July 2024
- Published by MDPI AG in Machine Learning and Knowledge Extraction
- Vol. 6 (3) , 1782-1797
Open AccessInsights from Augmented Data Integration and Strong Regularization in Drug Synergy Prediction with SynerGNet
SynerGNet is a novel approach to predicting drug synergy against cancer cell lines. In this study, we discuss in detail the construction process of SynerGNet, emphasizing its comprehensive design tailored to handle complex data patterns. Additionally, we investigate a counterintuitive phenomenon when integrating more augmented data into the training set results in an increase in testing loss alongside improved predictive accuracy. This sheds light on the nuanced dynamics of model learning. Further, we demonstrate the effectiveness of strong regularization techniques in mitigating overfitting, ensuring the robustness and generalization ability of SynerGNet. Finally, the continuous performance enhancements achieved through the integration of augmented data are highlighted. By gradually increasing the amount of augmented data in the training set, we observe substantial improvements in model performance. For instance, compared to models trained exclusively on the original data, the integration of the augmented data can lead to a 5.5% increase in the balanced accuracy and a 7.8% decrease in the false positive rate. Through rigorous benchmarks and analyses, our study contributes valuable insights into the development and optimization of predictive models in biomedical research.
- Open Access
- 21 February 2024
- Published by MDPI AG in Biomolecules
- Vol. 14 (3) , 253
SynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy
Drug combination therapy shows promise in cancer treatment by addressing drug resistance, reducing toxicity, and enhancing therapeutic efficacy. However, the intricate and dynamic nature of biological systems makes identifying potential synergistic drugs a costly and time-consuming endeavor. To facilitate the development of combination therapy, techniques employing artificial intelligence have emerged as a transformative solution, providing a sophisticated avenue for advancing existing therapeutic approaches. In this study, we developed SynerGNet, a graph neural network model designed to accurately predict the synergistic effect of drug pairs against cancer cell lines. SynerGNet utilizes cancer-specific featured graphs created by integrating heterogeneous biological features into the human protein–protein interaction network, followed by a reduction process to enhance topological diversity. Leveraging synergy data provided by AZ-DREAM Challenges, the model yields a balanced accuracy of 0.68, significantly outperforming traditional machine learning. Encouragingly, augmenting the training data with carefully constructed synthetic instances improved the balanced accuracy of SynerGNet to 0.73. Finally, the results of an independent validation conducted against DrugCombDB demonstrated that it exhibits a strong performance when applied to unseen data. SynerGNet shows a great potential in detecting drug synergy, positioning itself as a valuable tool that could contribute to the advancement of combination therapy for cancer treatment.
- 21 February 2024
- Published by MDPI AG in Biomolecules
- Vol. 14 (3) , 253
Open AccessSynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy
Drug combination therapy shows promise in cancer treatment by addressing drug resistance, reducing toxicity, and enhancing therapeutic efficacy. However, the intricate and dynamic nature of biological systems makes identifying potential synergistic drugs a costly and time-consuming endeavor. To facilitate the development of combination therapy, techniques employing artificial intelligence have emerged as a transformative solution, providing a sophisticated avenue for advancing existing therapeutic approaches. In this study, we developed SynerGNet, a graph neural network model designed to accurately predict the synergistic effect of drug pairs against cancer cell lines. SynerGNet utilizes cancer-specific featured graphs created by integrating heterogeneous biological features into the human protein–protein interaction network, followed by a reduction process to enhance topological diversity. Leveraging synergy data provided by AZ-DREAM Challenges, the model yields a balanced accuracy of 0.68, significantly outperforming traditional machine learning. Encouragingly, augmenting the training data with carefully constructed synthetic instances improved the balanced accuracy of SynerGNet to 0.73. Finally, the results of an independent validation conducted against DrugCombDB demonstrated that it exhibits a strong performance when applied to unseen data. SynerGNet shows a great potential in detecting drug synergy, positioning itself as a valuable tool that could contribute to the advancement of combination therapy for cancer treatment.