ArticleNAR genomics and bioinformatics2022
Matching anticancer compounds and tumor cell lines by neural networks with ranking loss.
Article in NAR genomics and bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Critical evaluation of drug response prediction models with DrEval.Nature communications · 2026Article
- Article
- Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.Journal of chemical information and modeling · 2024Article
- Overcoming limitations in current measures of drug response may enable AI-driven precision oncology.NPJ precision oncology · 2024Article
- Improving drug response prediction via integrating gene relationships with deep learning.Briefings in bioinformatics · 2024Article
- Deep learning methods for drug response prediction in cancer: Predominant and emerging trends.Frontiers in medicine · 2023Review
- Article
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Authors and funding
7 authors.
Funding
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Abstract
Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug's inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model's capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on
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Registered trials
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