Transformer-Based Deep Learning Framework for Predicting Stimuli-Responsive Release Profiles in Nano-Drug Delivery Systems
Venkateswaran Govindarajulu, Lokesh K · Dr. Yashwant Research Labs Pvt. Ltd. · 2026
A Transformer-based deep learning framework predicts drug release profiles from stimuli-responsive nanocarriers with high accuracy.
Plain English summary
Why this matters
Key findings
- Introduced a Transformer-based framework for predicting drug release kinetics.
- Achieved an R² score of 0.941 and a mean absolute error of 0.082.
- Outperformed traditional models like LSTM and GNNs.
- Utilized a dataset of eight different drugs across various responsive systems.
- Demonstrated potential for integration into clinical decision support.
What's new
The use of Transformer architecture for predicting drug release kinetics in stimuli-responsive systems is a novel approach that captures complex interactions better than previous models.
Limitations
The abstract does not discuss the limitations of the dataset or the generalizability of the model to other drug types or delivery systems.
Commercial context
The abstract does not provide information on commercial applications or readiness.
Publication
- Publisher
- Dr. Yashwant Research Labs Pvt. Ltd.
- Publication date
- May 30, 2026
- Research type
- Paper
Tags
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