Jang Woo Dae, Jang Jidon, Song Jin Sook, Ahn Sunjoo, Oh Kwang-Seok
Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea.
Department of Medicinal and Pharmaceutical Chemistry, University of Science and Technology, Daejeon 34129, Republic of Korea.
Comput Struct Biotechnol J. 2023 Jul 7;21:3532-3539. doi: 10.1016/j.csbj.2023.07.008. eCollection 2023.
Stability of compounds in the human plasma is crucial for maintaining sufficient systemic drug exposure and considered an essential factor in the early stages of drug discovery and development. The rapid degradation of compounds in the plasma can result in poor in vivo efficacy. Currently, there are no open-source software programs for predicting human plasma stability. In this study, we developed an attention-based graph neural network, PredPS to predict the plasma stability of compounds in human plasma using in-house and open-source datasets. The PredPS outperformed the two machine learning and two deep learning algorithms that were used for comparison indicating its stability-predicting efficiency. PredPS achieved an area under the receiver operating characteristic curve of 90.1%, accuracy of 83.5%, sensitivity of 82.3%, and specificity of 84.6% when evaluated using 5-fold cross-validation. In the early stages of drug discovery, PredPS could be a helpful method for predicting the human plasma stability of compounds. Saving time and money can be accomplished by adopting an in silico-based plasma stability prediction model at the high-throughput screening stage. The source code for PredPS is available at https://bitbucket.org/krict-ai/predps and the PredPS web server is available at https://predps.netlify.app.
化合物在人体血浆中的稳定性对于维持足够的全身药物暴露至关重要，并且被认为是药物发现和开发早期阶段的一个重要因素。血浆中化合物的快速降解会导致体内疗效不佳。目前，尚无用于预测人体血浆稳定性的开源软件程序。在本研究中，我们开发了一种基于注意力的图神经网络PredPS，用于使用内部和开源数据集预测化合物在人体血浆中的稳定性。PredPS的性能优于用于比较的两种机器学习算法和两种深度学习算法，表明了其稳定性预测效率。当使用五折交叉验证进行评估时，PredPS的受试者工作特征曲线下面积达到90.1%，准确率为83.5%，灵敏度为82.3%，特异性为84.6%。在药物发现的早期阶段，PredPS可能是一种预测化合物人体血浆稳定性的有用方法。在高通量筛选阶段采用基于计算机模拟的血浆稳定性预测模型可以节省时间和金钱。PredPS的源代码可在https://bitbucket.org/krict-ai/predps获取，PredPS网络服务器可在https://predps.netlify.app访问。
