Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems

Abstract

Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Nanotherapeutics, which merge nanotechnology with targeted drug delivery, exemplify this advancement by offering improved precision and reduced side effects in cancer therapy. The development of these nanotherapeutic agents depends critically on understanding nanoparticle (NP) properties and their biological interactions, often analyzed through molecular dynamics (MD) simulations. This study enhances these analyses by integrating ML with MD simulations, significantly improving both prediction accuracy and computational efficiency. We introduce a comprehensive three-stage methodology for predicting the solvent-accessible surface area (SASA) of NPs, which is crucial for their therapeutic efficacy. The process involves training an ML model to forecast the many-body tensor representation (MBTR) for future time steps, applying data augmentation to increase dataset realism, and refining the SASA predictor with both augmented and original data. Results demonstrate that our methodology can predict SASA values 299 time steps ahead with a 40-fold speed improvement and a 25% accuracy increase over existing methods. Importantly, it provides a 300-fold increase in computational speed compared to traditional simulation techniques, offering substantial cost and time savings for nanotherapeutic research and development.

Keywords: Cancer therapy; Data augmentation; Data science; Drug delivery; Machine learning; Molecular dynamics simulations; Nanotherapeutics.

Fig. 1

Schematic representation of the proposed method. The proposed methodology comprises three distinct phases: (a) MBTR model training: in this phase, 72 LR models are trained to predict the subsequent MBTR values. (b) Data augmentation: this phase involves the generation of augmented data to enhance the realism of the training data for the SASA model. (c) SASA model training: the final phase entails training an ETR model, which is capable of predicting the SASA value corresponding to each MBTR value.

Fig. 2

Flowchart of solving the main problem after addressing the sub-problems.

Fig. 3

The architecture of the proposed MBTR model.

Fig. 4

MAE comparison of LR with window sizes of 1 and 40 and the baseline for (a) all 72 features of MBTR and (b) the top 10 features with the highest errors for LR with a window size of 40.

Fig. 5

Prediction comparison of (a) the proposed method and (b) the baselineon the test sets designs.