Characterizing Clinical Toxicity in Cancer Combination Therapies DOI Creative Commons
Alexandra M. Wong, Lorin Crawford

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 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.

Language: Английский

Predicting drug and target interaction with dilated reparameterize convolution DOI Creative Commons
Min Deng, Jian Wang, Yiming Zhao

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 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.

Language: Английский

Citations

0
Opportunities and Challenges of Machine Learning in Anticaner Drug Therapies DOI Creative Commons

M.I.A.O. Chunlei,

H.U.A.N.G.F.U. rui,

Chao Yuan

et al.

Intelligent Pharmacy, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
Hallmarks of artificial intelligence contributions to precision oncology DOI
Tiangen Chang, Seongyong Park, Alejandro A. Schäffer

et al.

Nature Cancer, Journal Year: 2025, Volume and Issue: unknown

Published: March 7, 2025

Language: Английский

Citations

0
MGTNSyn: Molecular structure-aware graph transformer network with relational attention for drug synergy prediction DOI
Yunjiong Liu, Peiliang Zhang, Dongyang Li

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127699 - 127699

Published: April 1, 2025

Language: Английский

Citations

0
Characterizing Clinical Toxicity in Cancer Combination Therapies DOI Creative Commons
Alexandra M. Wong, Lorin Crawford

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 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.

Language: Английский

Citations

0