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TRUVACE RECORD VERSION
record: TRV-2026-1723
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-17T18:13:24.208767Z
status: published
lens: g_space
sector: science
headline: DeepFusion-ACSM: an interpretable hybrid ML-DL ensemble model for anticancer small-molecule activity prediction
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: achieved a macro-recall of 0.83, an accuracy of 0.83, and a ROC-AUC of 0.90, outperforming the individual ML and DL models
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers built a computational framework that fuses many machine learning and deep learning models to predict whether small molecules have anticancer activity. They trained 102 ML models and 12 DL models, then combined their out-of-fold predictions through eight ensemble strategies.

The result matters because drug discovery is described as costly and slow, and single-algorithm predictors often generalize poorly. The evidence is computational, with a small external check on 10 approved compounds, so real-world drug discovery benefit has not been shown in the supplied text.
limitation: External validation covered only 10 FDA-approved compounds, and the screening ranked candidates by predicted probability without reported experimental confirmation.
tag: Evidence-backed gain
key_points: The best ensemble combined XGBoost, Improved Deep MLP and TabNet, trained on the 2D descriptor dataset with mutual information feature selection. | It reached macro-recall 0.83, accuracy 0.83 and ROC-AUC 0.90, outperforming individual models. | External validation correctly labeled 8 of 10 FDA-approved compounds with known anticancer or non-anticancer status. | The framework was applied to 1200 FDA-approved drugs from the ZINC database for prospective virtual screening.
rundown: The study used 17 algorithms across two molecular descriptor datasets, a 2D set and a Hybrid set, with three feature selection strategies. Twelve deep learning models based on Improved Deep MLP, FT-Transformer and TabNet were optimized with five-fold cross-validation.

Out-of-fold predictions from the optimized models were merged by eight ensemble strategies to form ML, DL and hybrid ML-DL ensembles. The hybrid ensemble performed best.

For prospective screening, 1200 FDA-approved drugs from the ZINC database were ranked by ensemble prediction probability to prioritize candidates. The authors describe the framework as reproducible, interpretable and scalable.
sources:
- peer_reviewed | Journal of Computer-Aided Molecular Design | https://doi.org/10.1007/s10822-026-00956-6 | 2026-10-08
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