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Machine Learning in Bioinformatics · Journal article
PLOS One · August 13, 2026
Early or partial results. Treat as a signal, not a conclusion.
This computational study benchmarks six ranking loss functions and five molecular representations for predicting effective anticancer drugs on cell line screening data. Listwise methods (LambdaLoss, LambdaRank) outperformed alternatives, and an interpretability framework successfully aligned predictions with known biological features (ER status, docetaxel and triptolide substructures). This is a methods development and validation study with no prospective clinical evidence or patient outcome data.
Computational benchmarking study across multiple datasets with interpretability analysis. Cancer cell lines from two large-scale drug screening datasets (CTRP and PRISM). Intervention: Learning to Rank (LeToR) neural models with different loss functions and molecular feature representations. Compared with: Six ranking loss functions and five types of molecular representations compared across validation setups.
Listwise loss functions such as LambdaLoss and LambdaRank consistently excelled in both early and overall ranking quality Combining molecular fingerprints with physicochemical descriptors yielded improved performance Explainability pipeline successfully distinguished estrogen receptor-positive (ER⁺) and estrogen receptor-negative (ER−) breast cancer subtypes
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This work provides a framework for computational drug prioritization and demonstrates interpretability methods that align with known biology. However, it lacks prospective validation or clinical outcome evidence and should be viewed as a methodological contribution requiring independent validation before clinical application.
This is a benchmarking and methods development study using machine learning on existing drug-screening datasets with no prospective validation, clinical outcomes, or independent test cohort, demonstrating feasibility and interpretability of neural ranking models rather than efficacy.
As stated by the source record.
This work provides a framework for computational drug prioritization and demonstrates interpretability methods that align with known biology. However, it lacks prospective validation or clinical outcome evidence and should be viewed as a methodological contribution requiring independent validation before clinical application.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
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Learning to Rank (LeToR) methods have gained increasing attention in drug response prediction, offering a direct way to prioritize effective treatments for cancer cell lines. In this study, we systematically benchmark six ranking loss functions, including state-of-the-art listwise methods, and five types of molecular representations across two large-scale drug screening datasets, CTRP and PRISM. Using high-dimensional gene expression profiles and various drug fingerprints and descriptors, we evaluated models under multiple validation setups and ranking metrics. Our results demonstrate that listwise loss functions such as LambdaLoss and LambdaRank consistently excel in both early and overall ranking quality. Additionally, combining molecular fingerprints with physicochemical descriptors yielded improved performance. A novel attention-based mechanism and a modified version of RankingSHAP were integrated to enhance interpretability, uncovering key genes and substructures aligned with known biological insights. The explainability pipeline successfully distinguished estrogen receptor-positive (ER⁺) and estrogen receptor-negative (ER − ) breast cancer subtypes. The model successfully identified critical substructures in docetaxel, an FDA-approved therapy, and triptolide, which is currently undergoing clinical evaluation for breast cancer. These findings are consistent with established structure-activity relationship (SAR) data. Overall, this study presents a comprehensive evaluation framework and underscores the importance of carefully selecting loss functions and feature representations when developing robust and interpretable drug-ranking systems.
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