GA-XGBoost, an explainable AI technique, for analysis of thrombin inhibitory activity of diverse pool of molecules and supported by X-ray

The present work involves extreme gradient boosting in combination with shapley values, a thriving amalgamation under the terrain of Explainable artificial intelligence, along with genetic algorithm for the analysis of thrombin inhibitory activity of diverse pool of 2803 molecules. The methodology involves genetic algorithm for feature selection, followed by extreme gradient boosting analysis. The eight parametric genetic algorithm - extreme gradient boosting analysis has high statistical acceptance with R2tr = 0.895, R2L10%O = 0.900, and Q2F3 = 0.873. Shapley additive explanations, which provide each variable in a model an importance value, served as the foundation for the interpretation. Then, ceteris paribus approach involving comparison of counterfactual examples has been used to understand the influence of a structural feature on activity profile. The analysis indicates that aromatic carbon, ring/non-ring nitrogen in combination with other structural features govern the inhibitory profile. The genetic algorithm - extreme gradient boosting model's simplicity and predictions suggest that “Explainable AI” is useful in the future for identifying and using structural features in drug discovery. [Display omitted] •Use of explainable AI in conjugation with Genetic Algorithm and XGBoost regressor.•Global and local explanations along with use of easily interpretable molecular descriptors.•Novel, unreported and unique results for thrombin inhibitor development.•Multiple results supported by reported X-ray analysis.•A detailed mechanism discussed for multiple counter factual and matched molecular pairs of molecules.