Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders

• DOI: 10.1021/acs.molpharmaceut.9b00520
• Publication Date: 2019-10-16T21:54:10Z
• Authors: Matteo Manica; Ali Oskooei; Jannis Born; Vigneshwari Subra...; Julio Sáez-Rodríguez; María Rodríguez M...
• Keywords: 0301 basic medicine; FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Artificial Intelligence; drug sensitivity prediction; computational systems biology; machine learning; drug discovery; multiscale; multimodal; attention; CNN; RNN; explainability; interpretability; molecular networks; molecular fingerprints; GDSC; SMILES; gene expression; drug sensitivity; anticancer compounds; IC50; EC50; lead discovery; personalized medicine; precision medicine; GDSC; precision medicine; anticancer compounds,; Antineoplastic Agents; Machine Learning (stat.ML); IC50; Quantitative Biology - Quantitative Methods; RNN; Machine Learning (cs.LG); drug discovery; 03 medical and health sciences; Deep Learning; Statistics - Machine Learning; explainability; computational systems biology; Humans; molecular networks; drug sensitivity; lead discovery; Quantitative Methods (q-bio.QM); drug sensitivity prediction; molecular fingerprints; 0303 health sciences; multimodal; deep learning; personalized medicine; SMILES; anticancer compounds; attention; EC50; Artificial Intelligence (cs.AI); machine learning; multiscale; Drug Design; FOS: Biological sciences; gene expression; Neural Networks, Computer; interpretability; Algorithms; CNN

ISSN:1543-8384<br/>ISSN:1543-8392<br/>Molecular Pharmaceutics, 16 (12)<br/>