What question did this study set out to answer?

The research aims to discover inhibitors of TGFβR1 using machine learning and molecular dynamics techniques.

February 12, 2026Open Access

Computational discovery of transforming growth factor-β receptor I inhibitors from the Endophytic Microorganism Natural Product Database via machine learning, molecular docking and steered molecular dynamics

Puntos clave

The research aims to discover inhibitors of TGFβR1 using machine learning and molecular dynamics techniques.
Benchmarking eight ML algorithms for predicting binding free energies.
Virtual screening of candidates from the Endophytic Microorganism Natural Product Database.
Docking validation and molecular dynamics simulations to assess protein-ligand interactions.
Toxicity profiling to evaluate potential safety risks of the selected inhibitors.
KNN algorithm provided the best predictive accuracy for binding free energies.
Fourteen candidates were prioritized based on docking scores below -12 kcal mol−1.
Molecular dynamics simulations resulted in stable protein-ligand complexes.
Nigrosporolide exhibited the highest mechanical stability with ΔGSMD = -8.125 kcal mol−1.
Toxicity analysis yielded LD50 values ranging from 340 to 9000 mg kg−1.

Resumen

Abstracts Machine learning (ML) is increasingly applied in cheminformatics to predict physico-chemical and biological properties from structural descriptors. In this work, eight ML algorithms, Linear Regression, Random Forest, XGBoost, LightGBM, CatBoost, K-Nearest Neighbours (KNN), Support Vector Regression and Gradient Boosting, were benchmarked for predicting binding free energies of compounds targeting transforming growth factor-β receptor I (TGFβR1). Among them, KNN achieved the best predictive accuracy (RMSE = 1.023, R = 0.704, mean absolute error = 0.773) and was employed for virtual screening of the Endophytic Microorganism Natural Product Database. Fourteen candidates below −12 kcal mol−1 were prioritized. Docking validation confirmed reliability (root mean square deviation 2 Å), and five predicted hits displayed favourable docking scores. Molecular dynamics (MD) simulations over 100 ns revealed stable protein–ligand complexes, with interactions involving LYS232, LEU340, VAL219 and ILE211. To enhance energy prediction, steered MD (SMD) was applied, yielding stronger correlation with experimental data (R = −0.7201) compared with docking (R = 0.5404). Nigrosporolide showed the greatest mechanical stability (ΔGSMD = −8.125 kcal mol−1, ⟨Fmax⟩ = 852.78 pN). Toxicity profiling predicted median lethal dose (LD50) values from 340 to 9000 mg kg−1 with variable organ-specific risks. Overall, integrating ML, docking, SMD and toxicity analysis enabled effective identification of TGFβR1 inhibitors, highlighting nigrosporolide and trichocadinin E as promising leads for further study.

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