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April 24th, 2025
Version: 4
Departamento de Fisica de la Materia Condensada, Universidad Autonoma de Madrid, 28049 Madrid, Spain
bioinformatics
biorxiv

Optimizing drug synergy prediction through categorical embeddings in Deep Neural Networks

Gonzalez Lastre, M.Open in Google Scholar•Gonzalez de Prado Salas, P.Open in Google Scholar•Guantes, R.Open in Google Scholar

Cancer treatments often lose effectiveness as tumors develop resistance to single-agent therapies. Combination treatments can overcome this limitation, but the overwhelming combinatorial space of drug-dose interactions makes exhaustive experimental testing impractical. Data-driven methods, such as Machine and Deep Learning, have emerged as promising tools to predict synergistic drug combinations. In this work, we benchmark the use of categorical embeddings within Deep Neural Networks to enhance drug synergy predictions. These learned and transferable encodings capture similarities between the elements of each category, demonstrating particular utility in scarce data scenarios.

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