<ul data-eligibleForWebStory="false"><li>Venlafaxine, commonly used for depression and anxiety disorders, is likely a serotonin-norepinephrine reuptake inhibitor with potential CNS activity based on its structure.</li><li>Research to validate its biological targets integrates experimental, computational, and multiomics approaches like computational target prediction and high-throughput screening.</li><li>Machine learning enhances prediction of target druggability by integrating diverse protein features, addressing data imbalance, and predicting drug-target interactions for faster drug discovery.</li><li>ML models can predict side effects, prioritize targets for specific diseases, and improve the efficiency of drug development by scoring and ranking targets based on predicted therapeutic effects.</li></ul>

Venlafaxine: assessing the potential for repurposing.

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