Exploration of BioMat-X

Table 5. Comparative analysis of modeling approaches.

Modeling approach	Strengths	Limitations	Applications
Conventional models	Simple, widely validated, and effective for initial screening.	Limited ability to capture complex or non-linear release kinetics.	Lipid-based nanoparticles, polymeric systems, and empirical studies.
Machine learning [e.g., decision tree regression (DTR)]	High predictive accuracy and ability to handle complex datasets.	Requires large datasets, potential overfitting.	Advanced drug delivery systems, data-rich environments.
Mechanistic models	Captures underlying physical and chemical processes, high precision.	Complex to develop, it requires detailed system-specific knowledge.	Implantable systems, long-term release platforms.
Hyperbolic tangent function	Robust for non-linear and complex release kinetics.	Less commonly used, requires validation for new systems.	Polymeric nanoparticles, systems with multi-phase release profiles.

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Declarations

Author contributions

AB: Conceptualization, Data curation, Investigation, Visualization, Writing—original draft. YA: Resources, Methodology, Software, Validation, Writing—review & editing. TKB: Conceptualization, Software, Supervision, Writing—original draft, Writing—review & editing. MTI: Conceptualization, Supervision, Project administration, Writing—review & editing. All authors read and approved the submitted version.

Conflicts of interest

The authors declare no conflicts of interest.

Ethical approval

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The author(s) received no financial support for the research, authorship, and/or publication of this article.

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