Xiao Meisheng, Zheng Qianhui, Popa Paul, Mi Xinlei, Hu Jianhua, Zou Fei, Zou Baiming
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, USA.
System2, New York, NY, USA.
Sci Rep. 2025 Jan 2;15(1):20. doi: 10.1038/s41598-024-84711-7.
The integration of drug molecular representations into predictive models for Drug Response Prediction (DRP) is a standard procedure in pharmaceutical research and development. However, the comparative effectiveness of combining these representations with genetic profiles for DRP remains unclear. This study conducts a comprehensive evaluation of the efficacy of various drug molecular representations employing cutting-edge machine learning models under various experimental settings. Our findings reveal that the inclusion of molecular representations from either PubChem fingerprints or SMILES can significantly enhance the performance of DRPs when used in conjunction with deep learning models. However, the optimal choice of drug molecular representation can vary depending on the predictive model and the specific DRP task. The insights derived from our study offer useful guidance on selecting the most suitable drug molecular representations for constructing efficient predictive models for DRPs, aiding for drug repurposing, personalized medicine, and new drug discovery.
将药物分子表征整合到药物反应预测（DRP）的预测模型中是药物研发中的标准程序。然而，将这些表征与基因图谱相结合用于DRP的相对有效性仍不明确。本研究在各种实验设置下，使用前沿机器学习模型对各种药物分子表征的功效进行了全面评估。我们的研究结果表明，当与深度学习模型结合使用时，包含来自PubChem指纹或SMILES的分子表征可以显著提高DRP的性能。然而，药物分子表征的最佳选择可能因预测模型和特定的DRP任务而异。我们研究得出的见解为选择最合适的药物分子表征以构建高效的DRP预测模型提供了有用的指导，有助于药物再利用、个性化医疗和新药发现。
