Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China.
J Biomol Struct Dyn. 2024 Jul;42(11):5946-5962. doi: 10.1080/07391102.2023.2259511. Epub 2023 Oct 12.
The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.934 for the prior model and 0.830 for the discriminating model, outperforming inverse docking in predicting protein-ligand interactions. When comparing with other open-source target identification tools, it was found that only our tool was able to accurately predict all the targets corresponding to each compound. This further demonstrates the ability of our strategy to take full advantage of molecular-level information as well as protein-level information in molecular activity prediction. Our work highlights the contribution of machine learning in the identification of potential epigenetic targets and offers a novel approach for epigenetic drug discovery and development.Communicated by Ramaswamy H. Sarma.
确定已知生物活性化合物的潜在表观遗传靶标至关重要且具有广阔前景，因为越来越多的表观遗传药物用于癌症临床治疗，与表观遗传学相关的化学生物基因组数据可用性也在增加。在这项研究中，我们引入了一种新的表观遗传靶标识别策略（ETI-Strategy），该策略结合了多任务图卷积神经网络先验模型和蛋白质-配体相互作用分类判别模型，使用大规模生物活性数据对 55 个表观遗传靶标进行分析。我们的方法利用机器学习技术实现了先验模型 AUC 值为 0.934，判别模型 AUC 值为 0.830，优于反向对接在预测蛋白质-配体相互作用方面的表现。与其他开源靶标识别工具进行比较时，发现只有我们的工具能够准确预测每个化合物对应的所有靶标。这进一步证明了我们的策略能够充分利用分子水平和蛋白质水平的信息进行分子活性预测。我们的工作强调了机器学习在潜在表观遗传靶标识别中的贡献，并为表观遗传药物发现和开发提供了一种新方法。由 Ramaswamy H. Sarma 交流。
