Hakim Sadra, Ngom Alioune
School of Computer Science, University of Windsor, Windsor, ON, Canada.
Front Pharmacol. 2025 Jul 31;16:1617142. doi: 10.3389/fphar.2025.1617142. eCollection 2025.
Polypharmacy, the concurrent use of multiple drugs, is a common approach to treating patients with complex diseases or multiple conditions. Although consuming a combination of drugs can be beneficial in some cases, it can lead to unintended drug-drug interactions (DDI) and increase the risk of adverse side effects. Predicting these adverse side effects using state-of-the-art models like Large Language Models (LLMs) can greatly assist clinicians. In this study, we assess the impact of using different LLMs to predict polypharmacy. First, the chemical structure of drugs is vectorized using several LLMs such as ChemBERTa, GPT, etc., and are then combined to obtain a single representation for each drug pair. The drug pair representation is then fed into two separate models including a Multilayer Perceptron (MLP) and a Graph Neural Network (GNN) to predict the side effects. Our experimental evaluations show that integrating the embeddings of Deepchem ChemBERTa with the GNN architecture yields more effective results than other methods. Additionally, we demonstrated that utilizing complex models like LLMs to predict polypharmacy side effects using only chemical structures of drugs can be highly effective, even without incorporating other entities such as proteins or cell lines, which is particularly advantageous in scenarios where these entities are not available.
多重用药，即同时使用多种药物，是治疗患有复杂疾病或多种病症患者的常用方法。尽管在某些情况下服用多种药物组合可能有益，但它可能导致意外的药物相互作用（DDI）并增加不良副作用的风险。使用诸如大语言模型（LLMs）等先进模型预测这些不良副作用可以极大地帮助临床医生。在本研究中，我们评估使用不同的大语言模型预测多重用药的影响。首先，使用诸如ChemBERTa、GPT等多种大语言模型将药物的化学结构向量化，然后将其组合以获得每个药物对的单一表示。然后将药物对表示输入到两个单独的模型中，包括多层感知器（MLP）和图神经网络（GNN），以预测副作用。我们的实验评估表明，将Deepchem ChemBERTa的嵌入与GNN架构相结合比其他方法产生更有效的结果。此外，我们证明，仅使用药物的化学结构，利用诸如大语言模型这样的复杂模型预测多重用药副作用可能非常有效，即使不纳入其他实体，如蛋白质或细胞系，这在这些实体不可用的情况下尤其有利。
