School of Software, Shandong University, Jinan, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.
Shaoxing Healthcare Security Bureau, China.
Comput Biol Med. 2023 Sep;164:106904. doi: 10.1016/j.compbiomed.2023.106904. Epub 2023 May 14.
Drug toxicity prediction is essential to drug development, which can help screen compounds with potential toxicity and reduce the cost and risk of animal experiments and clinical trials. However, traditional handcrafted feature-based and molecular-graph-based approaches are insufficient for molecular representation learning. To address the problem, we developed an innovative molecular fingerprint Graph Transformer framework (MolFPG) with a global-aware module for interpretable toxicity prediction. Our approach encodes compounds using multiple molecular fingerprinting techniques and integrates Graph Transformer-based molecular representation for feature learning and toxic prediction. Experimental results show that our proposed approach has high accuracy and reliability in predicting drug toxicity. In addition, we explored the relationship between drug features and toxicity through an interpretive analysis approach, which improved the interpretability of the approach. Our results highlight the potential of Graph Transformers and multi-level fingerprints for accelerating the drug discovery process by reliably, effectively alarming drug safety. We believe that our study will provide vital support and reference for further development in the field of drug development and toxicity assessment.
药物毒性预测对于药物开发至关重要，它可以帮助筛选具有潜在毒性的化合物，降低动物实验和临床试验的成本和风险。然而，传统的基于手工特征和基于分子图的方法在分子表示学习方面还不够充分。为了解决这个问题，我们开发了一种创新的分子指纹图转换器框架（MolFPG），该框架具有一个全局感知模块，用于可解释的毒性预测。我们的方法使用多种分子指纹技术对化合物进行编码，并集成基于图转换器的分子表示进行特征学习和毒性预测。实验结果表明，我们提出的方法在预测药物毒性方面具有较高的准确性和可靠性。此外，我们还通过解释性分析方法探索了药物特征与毒性之间的关系，从而提高了方法的可解释性。我们的研究结果强调了图转换器和多层次指纹在通过可靠、有效地警告药物安全性来加速药物发现过程方面的潜力。我们相信，我们的研究将为药物开发和毒性评估领域的进一步发展提供重要的支持和参考。
