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  • Network approaches for modeling the effect of drugs and . . .
    Network-based approaches have also been used to predict drug sensitivity and synergy Synergistic drugs have potential to kill tumor cells more effectively while requiring lower doses; however, sensitivity and synergy are cancer specific, which makes drug combination particularly challenging
  • Open MoA: revealing the mechanism of action (MoA) based on . . .
    Using a multi-omics integrated network, which contains information of various biological interactions, could offer a more comprehensive inspective and interpretation for the drug mechanism of action (MoA) We developed a computational pipeline for dissecting the hidden MoAs of drugs (Open MoA)
  • Discovering the mechanism of action of drugs with a sparse . . .
    We developed SparseGO, a sparse and interpretable neural network, for predicting drug response in cancer cell lines and their Mechanism of Action (MoA) To ensure model generalization, we trained it on multiple datasets and evaluated its performance using three cross-validation schemes
  • DTIAM: a unified framework for predicting drug-target . . .
    Accurate and robust prediction of drug-target interactions (DTIs) plays a vital role in drug discovery but remains challenging due to limited labeled data, cold start problems, and
  • Predicting the effects of drugs and unveiling their . . . - PubMed
    Additionally, a graph neural network (GNN)-based approach was used to predict therapeutic medication and indication, which outperformed previous approaches that relied on correlation-based knowledge graphs lacking pharmacodynamic MOA representations
  • Network approaches for modeling the effect of drugs and diseases
    In this review, we describe network data mining algorithms that are commonly used to study drug’s MoA and to improve our understanding of the basis of chronic diseases
  • Deep Learning Approaches for Predicting Drug Mechanisms of . . .
    models that simultaneously predict all mechanisms of action can do just that We propose a “sequential” neural network that first predicts a drug’s type of interaction (e g , inhibitor, agonist, antagonist, etc ) and then uses the predicted interactions to predict the mechanism of action We find that our neural netw





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