ArticleScientific reports2023
Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solution.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Review
- Advances in improving cancer immunotherapy with nanotechnology: from smart nanoparticles to synergistic combination strategies.Molecular cancer · 2026Review
- Artificial Intelligence-Driven Nanomedicine: From Drug Formulation and Nanocarrier Design to Clinical Translation.Pharmaceutics · 2026Review
- Artificial Intelligence-Driven Nanoarchitectonics for Smart Targeted Drug Delivery.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Advancing engineering design strategies for targeted cancer nanomedicine.Nature reviews. Cancer · 2025Review
- Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- The Nanocarrier Landscape─Evaluating Key Drug Delivery Vehicles and Their Capabilities: A Translational Perspective.ACS applied materials & interfaces · 2025Review
- Nanotechnology-Driven Drug Delivery Systems for Lung Cancer: Computational Advances and Clinical Perspectives.Thoracic cancer · 2025Review
- Numerical Simulation in Microvessels for the Design of Drug Carriers with the Immersed Boundary-Lattice Boltzmann Method.Micromachines · 2025Article
- The anticancer activity of fucoidan coated selenium nanoparticles and curcumin nanoparticles against colorectal cancer lines.Scientific reports · 2025Article
- Decoding the Knacks of Ellagitannin Lead Compounds to Treat Nonalcoholic Fatty Liver Disease using Computer-aided Drug Designing.Current computer-aided drug design · 2025Article
- Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems.Scientific reports · 2024Article
- Point biserial correlation symbiotic organism search nanoengineering based drug delivery for tumor diagnosis.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular Dynamic (MD) simulations are very effective in the discovery of nanomedicines for treating cancer, but these are computationally expensive and time-consuming. Existing studies integrating machine learning (ML) into MD simulation to enhance the process and enable efficient analysis cannot provide direct insights without the complete simulation. In this study, we present an ML-based approach for predicting the solvent accessible surface area (SASA) of a nanoparticle (NP), denoting its efficacy, from a fraction of the MD simulations data. The proposed framework uses a time series model for simulating the MD, resulting in an intermediate state, and a second model to calculate the SASA in that state. Empirically, the solution can predict the SASA value 260 timesteps ahead 7.5 times faster with a very low average error of 1956.93. We also introduce the use of an explainability technique to validate the predictions. This work can reduce the computational expense of both processing and data size greatly while providing reliable solutions for the nanomedicine design process.
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