ToxCML: A Hybrid mfCoQ-RASAR-Based Platform Integrating Consensus QSAR and Read-Across for Comprehensive Multi-End Point Toxicity Assessment

Computational in silico methods offer a powerful alternative
to
animal-based toxicity testing, which remains time-consuming, expensive,
and ethically challenging. In practice, QSAR and read-across (RA)
are among the most widely explored approaches, yet both suffer from
important limitations, including end point-specific modeling, restricted
applicability domains, nonquantitative or subjective analogue selection,
and sensitivity to biases in chemical space coverage. To address these
limitations, this study introduces ToxCML, a large-scale hybrid multifeature
consensus quantitative RA Structure–Activity Relationship (mfCoQ-RASAR)
platform that unifies consensus QSAR and similarity-based consensus
RA into a weight-optimized workflow for predicting 18 toxicity end
points across 54,601 unique chemicals. The framework combines multiple
molecular representations (MACCS, Morgan, atom pair fingerprints,
RDKit fingerprints, and physicochemical descriptors) with machine-learning-based
QSAR and k-NN RA models and incorporates tiered applicability-domain
analysis and chemical-space mapping that indicate broadly overlapping
training-test distributions and in-domain coverage generally exceeding
95% across end points. Across all end points and evaluation settings
involving unseen test sets or external validation sets, the hybrid
mfCoQ-RASAR models achieve strong discrimination and accuracy (AUC
approximately 0.86–0.99; BACC 0.73–0.98), with a consistent
performance hierarchy in which mfCoQ-RASAR provides the highest or
near-highest performance, consensus QSAR remains highly competitive,
and consensus RA, while weaker, still offers informative predictive
and analogue-based discriminatory capability. These results indicate
that our framework delivers reliable, chemically well-contextualized,
and broadly applicable multiend point toxicity predictions for unseen
compounds and may support large-scale toxicity screening, hazard prioritization,
and efforts to reduce animal testing in regulatory and industrial
settings. The ToxCML platform is accessible through a public web server
available at http://cardiosim.metaheart.kr:8080/.