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Author

S. Tiwari

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Open access Aug 2026

Domain-Knowledge-Guided Feature Engineering for Small-Sample Machine Learning Prediction of Mechanical Properties in Low-Carbon Hot-Rolled Steel Strips

Industrial steel property prediction is often constrained by limited labelled data, reducing the effectiveness of conventional machine learning models. This study investigated whether metallurgy-informed feature engineering enhances predictive performance under small-data conditions. A representative set of 300 samples...

S. Tiwari, H. Ahn, Jongwon Lee et al. · 0 citations
Open access Aug 2026

Quantitative Structural Thresholds for Blood–Brain Barrier Permeability Derived from Experimental logBB Measurements

These findings suggest that the commonly applied TPSA threshold of 90 Å2 may be overly lenient, and experimentally grounded rules offer a practical framework for the early-stage design of CNS leads.

S. Tiwari, Katarzyna Mądra-Gackowska, Marcin Gackowski et al. · 0 citations
Open access Aug 2026

Evaluating the Incremental Value of Three-Dimensional Conformer Descriptors for Blood–Brain Barrier Permeability Prediction Using the B3DB Benchmark Dataset

3D conformer descriptors provided limited incremental value and no scaffold-robust advantage over well-curated 2D molecular representations for BBB permeability predictions, highlighting the central role of desolvation in BBB permeability predictions.

S. Tiwari, Katarzyna Mądra-Gackowska, Marcin Gackowski et al. · 0 citations
Open access Sep 2026

Interpretable Machine Learning for Mechanical Property Prediction of 5Cr-0.5Mo Steel: SHAP Explainability, Multi-Model Comparison, and Uncertainty Quantification

Three tree-based machine learning models were developed using 36 unique alloy grade–temperature observations from a validated NIMS 5Cr-0.5Mo tensile dataset, demonstrating the potential of interpretable, uncertainty-aware ML for small alloy datasets, while emphasizing the need for larger, compositionally diverse datase...

S. Tiwari, H. Ahn, Jongwon Lee et al. · 0 citations
Review Open access Sep 2026

Integrating Artificial Intelligence with Emerging Pharmaceutical Technologies: Current Progress, Clinical Translation, and Future Challenges

Overall, these technologies show meaningful potential to shorten development time and improve treatment safety, although further prospective validation is required before realizing this potential at scale.

Priya Sharma, S. Tiwari, Nokeun Park et al. · 0 citations

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