Characterizing Clinical Toxicity in Cancer Combination Therapies

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Апрель 19, 2025

A bstract Predicting synergistic cancer drug combinations through computational methods offers a scalable approach to creating therapies that are more effective and less toxic. However, most algorithms focus solely on synergy without considering toxicity when selecting optimal combinations. In the absence of combinatorial assays, few models use penalties balance high with lower toxicity. these have not been explicitly validated against known drug-drug interactions. this study, we examine whether scores metrics correlate adverse While some show trends levels, our results reveal significant limitations in using them as penalties. These findings highlight challenges incorporating into prediction frameworks suggest advancing field requires comprehensive combination data.

Язык: Английский

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Machine learning models for predicting hospitalization and mortality risks of COVID-19 patients

Expert Systems with Applications, Год журнала: 2023, Номер 240, С. 122670 - 122670

Опубликована: Ноя. 19, 2023

Язык: Английский

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New methods for drug synergy prediction: A mini-review

Current Opinion in Structural Biology, Год журнала: 2024, Номер 86, С. 102827 - 102827

Опубликована: Май 4, 2024

In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of field is witnessed in more than thirty original machine learning published since 2021, a clear majority them based deep techniques. We aim to put these articles under unifying lens by highlighting core technologies, data sources, input types and scores used methods, as well scenarios evaluation protocols that deal with. Our finding best accurately solve involving known drugs or cell lines while still fall short an accurate level.

Язык: Английский

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Predicting drug and target interaction with dilated reparameterize convolution

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 20, 2025

Predicting drug-target interaction (DTI) stands as a pivotal and formidable challenge in pharmaceutical research. Many existing deep learning methods only learn the high-dimensional representation of ligands targets on small scale. However, it is difficult for model to obtain potential law combining pockets or multiple binding sites large To address this lacuna, we designed large-kernel convolutional block extracting large-scale sequence information proposed novel DTI prediction framework, named Rep-ConvDTI. The reparameterization method introduced help convolutions capture small-scale information. We have also developed gated attention mechanism more efficiently characterize drugs targets. Extensive experiments demonstrate that Rep-ConvDTI achieves most competitive performance against state-of-the-art baselines three benchmark datasets. Furthermore, validated drug screening tool through interpretative studies with cystathionine-β-synthase.

Язык: Английский

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Opportunities and Challenges of Machine Learning in Anticaner Drug Therapies

Intelligent Pharmacy, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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