<ul><li>Virtual screening plays a critical role in modern drug discovery by enabling the identification of promising candidate molecules for experimental validation.</li><li>Traditional machine learning methods such as support vector machines (SVM) and XGBoost rely on predefined molecular representations, often leading to information loss and potential bias.</li><li>In contrast, utilizing Graph Convolutional Networks (GCNs) and Large Language Models (LLMs) can provide a more expressive and unbiased alternative by operating directly on molecular graphs and capturing complex chemical patterns.</li><li>A hybrid architecture that integrates GCNs with LLM-derived embeddings has been proposed, achieving superior results and outperforming standalone GCN, XGBoost, and SVM baselines.</li></ul>

Combining GCN Structural Learning with LLM Chemical Knowledge for or Enhanced Virtual Screening

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