Abouzied Amr S, Alshammari Bahaa, Kari Hayam, Huwaimel Bader, Alqarni Saad, Kassab Shaymaa E
Department of Pharmaceutical Chemistry, College of Pharmacy, University of Hail, 81442, Hail, Saudi Arabia.
Department of Pharmaceutical Chemistry, Egyptian Drug Authority, Giza, Egypt.
Mol Divers. 2024 Oct 19. doi: 10.1007/s11030-024-11011-7.
Proteolysis Targeting Chimeras are part of targeted protein degradation (TPD) techniques, which are significant for pharmacological and therapy development. Small-molecule interaction with the targeted protein is a complicated endeavor and a challenge to predict the proteins accurately. This study used machine learning algorithms and molecular fingerprinting techniques to build an AI-powered PROTAC Activity Prediction Tool that could predict PROTAC activity by examining chemical structures. The chemical structures of a diverse set of PROTAC drugs and their corresponding activities are selected as a dataset for training the tool. The processes used in this study included data preparation, feature extraction, and model training. Further, evaluation was done for the performance of the various classifiers, such as AdaBoost, Support Vector Machine, Random Forest, Gradient Boosting, and Multi-Layer Perceptron. The findings show that the methods selected here depict accurate PROTAC activities. All the models in this study showed an ROC curve better than 0.9, while the random forest on the test set of the AI-DPAPT had an area under the curve score of 0.97, thus showing accurate results. Furthermore, the study revealed significant insights into the molecular features that can influence the functions of the PROTAC. These findings can potentially increase the understanding of the structure-activity correlations involved in the TPD. Overall, the investigation contributes to computational drug development by introducing this platform powered by artificial intelligence that predicts the function of PROTAC. In addition, it sped up the processes of identifying and improving previously unknown medications. The AI-DPAPT platform can be accessed online using a web server at https://ai-protac.streamlit.app/ .
蛋白酶靶向嵌合体是靶向蛋白质降解（TPD）技术的一部分，这对于药理学和治疗学发展具有重要意义。小分子与靶向蛋白的相互作用是一项复杂的工作，准确预测蛋白质具有挑战性。本研究使用机器学习算法和分子指纹技术构建了一个由人工智能驱动的PROTAC活性预测工具，该工具可以通过检查化学结构来预测PROTAC活性。选择了一组多样化的PROTAC药物的化学结构及其相应活性作为训练该工具的数据集。本研究中使用的过程包括数据准备、特征提取和模型训练。此外，还对各种分类器（如AdaBoost、支持向量机、随机森林、梯度提升和多层感知器）的性能进行了评估。研究结果表明，这里选择的方法能够准确描述PROTAC活性。本研究中的所有模型的ROC曲线均优于0.9，而人工智能驱动的PROTAC活性预测工具（AI-DPAPT）测试集上的随机森林曲线下面积得分为0.97，从而显示出准确的结果。此外，该研究揭示了对可能影响PROTAC功能的分子特征的重要见解。这些发现可能会增加对TPD中结构-活性相关性的理解。总体而言，该研究通过引入这个由人工智能驱动的预测PROTAC功能的平台，为计算药物开发做出了贡献。此外，它加快了识别和改进以前未知药物的过程。可以通过https://ai-protac.streamlit.app/的网络服务器在线访问AI-DPAPT平台。
