PLASMOpred: A Machine Learning-Based Web Application for Predicting Antimalarial Small Molecules Targeting the Apical Membrane Antigen 1–Rhoptry Neck Protein 2 Invasion Complex
Abstract
1. Introduction
2. Results and Discussion
2.1. Data Collection and Pre-Processing
2.2. Descriptor Generation and Feature Engineering
2.3. Data Splitting and Data Balancing
2.4. Development and Evaluation of Machine Learning Models
2.5. Validation of Machine Learning Models
2.6. Model Deployment
3. Applicability Domain
4. Materials and Methods
4.1. Data Collection
4.2. Descriptor Generation and Feature Engineering
4.3. Development and Evaluation of Machine Learning Models
4.4. Validation of Machine Learning Models
4.5. Model Deployment
5. Limitations
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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METRICS | MODELS | ||||
---|---|---|---|---|---|
RF | GBMs | CB | AB | SVM | |
Sensitivity | 0.85 | 0.85 | 0.87 | 0.77 | 0.86 |
Specificity | 0.88 | 0.90 | 0.88 | 0.92 | 0.80 |
Precision | 0.81 | 0.84 | 0.80 | 0.85 | 0.71 |
F1-score | 0.83 | 0.84 | 0.84 | 0.81 | 0.78 |
Accuracy | 0.87 | 0.89 | 0.88 | 0.86 | 0.82 |
ROC-AUC | 0.91 | 0.92 | 0.93 | 0.93 | 0.90 |
METRICS | MODELS | ||||
---|---|---|---|---|---|
RF | GBMs | CB | AB | SVM | |
Sensitivity | 0.80 | 0.85 | 0.85 | 0.80 | 0.50 |
Specificity | 0.85 | 0.95 | 0.85 | 0.90 | 0.80 |
Precision | 0.84 | 0.94 | 0.85 | 0.88 | 0.71 |
F1-score | 0.82 | 0.89 | 0.85 | 0.84 | 0.59 |
Accuracy | 0.83 | 0.90 | 0.85 | 0.85 | 0.65 |
ROC-AUC | 0.80 | 0.85 | 0.85 | 0.80 | 0.50 |
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Lamptey, E.; Oparebea, J.; Anyaele, G.; Ofosu, B.; Hanson, G.; Sakyi, P.O.; Agyapong, O.; Amuzu, D.S.Y.; Miller, W.A., III; Kwofie, S.K.; et al. PLASMOpred: A Machine Learning-Based Web Application for Predicting Antimalarial Small Molecules Targeting the Apical Membrane Antigen 1–Rhoptry Neck Protein 2 Invasion Complex. Pharmaceuticals 2025, 18, 776. https://doi.org/10.3390/ph18060776
Lamptey E, Oparebea J, Anyaele G, Ofosu B, Hanson G, Sakyi PO, Agyapong O, Amuzu DSY, Miller WA III, Kwofie SK, et al. PLASMOpred: A Machine Learning-Based Web Application for Predicting Antimalarial Small Molecules Targeting the Apical Membrane Antigen 1–Rhoptry Neck Protein 2 Invasion Complex. Pharmaceuticals. 2025; 18(6):776. https://doi.org/10.3390/ph18060776
Chicago/Turabian StyleLamptey, Eugene, Jessica Oparebea, Gabriel Anyaele, Belinda Ofosu, George Hanson, Patrick O. Sakyi, Odame Agyapong, Dominic S. Y. Amuzu, Whelton A. Miller, III, Samuel K. Kwofie, and et al. 2025. "PLASMOpred: A Machine Learning-Based Web Application for Predicting Antimalarial Small Molecules Targeting the Apical Membrane Antigen 1–Rhoptry Neck Protein 2 Invasion Complex" Pharmaceuticals 18, no. 6: 776. https://doi.org/10.3390/ph18060776
APA StyleLamptey, E., Oparebea, J., Anyaele, G., Ofosu, B., Hanson, G., Sakyi, P. O., Agyapong, O., Amuzu, D. S. Y., Miller, W. A., III, Kwofie, S. K., & Mensah-Brown, H. E. (2025). PLASMOpred: A Machine Learning-Based Web Application for Predicting Antimalarial Small Molecules Targeting the Apical Membrane Antigen 1–Rhoptry Neck Protein 2 Invasion Complex. Pharmaceuticals, 18(6), 776. https://doi.org/10.3390/ph18060776