Ensemble learning-guided discovery of anti-tuberculosis phytochemicals: computational prediction and mechanistic insights

Clinical Snapshot

Key Findings

• P Value: Not applicable for computational study

• Effect Size: 3209 predicted actives (68.2% hit rate) with 778 meeting applicability domain criteria

• Primary Outcome: Identification of 778 high-confidence anti-TB phytochemical candidates from 4707 screened compounds

• Nnt Or Sensitivity: ROC-AUC: 0.983, Matthews Correlation Coefficient: 0.871, 486 compounds with favorable binding affinities

• Confidence Interval: Not applicable for computational predictions

Clinical Application

Requires experimental validation and medicinal chemistry optimization before clinical application Highly relevant given Australia's TB burden in Indigenous communities and immigrant populations; aligns with TGA's interest in natural product-derived therapeutics Patients with drug-susceptible and drug-resistant tuberculosis globally

Abstract

Tuberculosis (TB) remains a major global health challenge driven by persistent Mycobacterium tuberculosis infection and increasing drug resistance. Phytochemicals represent a structurally diverse and underexplored chemical space for anti-TB drug discovery, yet systematic prioritization strategies integrating machine learning and structure-based validation are limited. A curated phenotypic anti-TB dataset of 425,180 compounds was used to train ensemble ExtraTrees models based on ECFP4 fingerprints and physicochemical descriptors. The models achieved strong predictive performance (ROC-AUC up to 0.983; MCC up to 0.871). SHAP analysis enabled mechanistic interpretation by identifying the key molecular descriptors and fingerprint features driving anti-TB activity predictions. The validated ensemble was applied to screen 4707 phytochemicals, yielding 3209 predicted actives, of which 778 satisfied applicability domain criteria. High-confidence candidates were subsequently evaluated by molecular docking against twelve structurally validated essential M. tuberculosis targets spanning cell wall biosynthesis, energy metabolism, nucleotide synthesis, and cofactor pathways. Docking analysis identified 486 phytochemicals with favorable predicted binding affinities, including 193 compounds exhibiting multi-target engagement. Several top-ranked candidates reproduced canonical interaction patterns of co-crystallized inhibitors, supporting mechanistic plausibility. This integrated chemoinformatics and structure-based framework enables robust prioritization of phytochemicals with biologically meaningful and multi-target antitubercular potential. The study provides a computationally grounded strategy for accelerating lead identification against drug-resistant TB.

References

1. 1.Sajal, Harshit, Mohan, Aswin, Raju, Rajesh, & Nadh, Anuroopa G. (2026). Ensemble learning-guided discovery of anti-tuberculosis phytochemicals: computational prediction and mechanistic insights. Journal of Computer-Aided Molecular Design. https://doi.org/10.1126/sciadv.1500106