aceDeepFusion-ACSM: an interpretable hybrid ML-DL ensemble model for anticancer small-molecule activity prediction
Cancer remains one of the leading causes of mortality worldwide, and the discovery of novel anticancer drugs continues to be a costly and time-consuming process. Although machine learning (ML) and deep learning (DL) have significantly advanced computational drug discovery, many existing predictive models rely on a single learning algorithm and often fail to generalize across diverse molecular descriptors, limiting their predictive reliability. In this study, we propose an interpretable hybrid ML-DL ensemble fram…
Combining XGBoost, a deep MLP and TabNet in an ensemble identified anticancer small molecules more accurately than individual ML or DL models, which could speed early-stage drug discovery.
External validation covered only 10 FDA-approved compounds, and the screening ranked candidates by predicted probability without reported experimental confirmation.
Evidence
- Peer-reviewedJournal of Computer-Aided Molecular Design2026-10-08
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Truvace Impact Record TRV-2026-1723, v1: “DeepFusion-ACSM: an interpretable hybrid ML-DL ensemble model for anticancer small-molecule activity prediction.” Truvace, 2026-10-17. /record/TRV-2026-1723 (accessed at citation time). sha256 8fea26d21d51be4b…
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