arXiv 2302.078682025-05-26cs.LGq-bio.BMq-bio.QM

基于图Transformer生成对抗网络的靶标特异性药物候选分子从头设计

Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks

Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University（哈切塔佩大学计算机工程系生物数据科学实验室）；Dept. of Computer Engineering, Hacettepe University（哈切塔佩大学计算机工程系）；Dept. of Chemistry, Middle East Technical University（中东技术大学化学系）；Dept. of Physics, Middle East Technical University（中东技术大学物理系）；Dept. Of Artificial Intelligence Engineering, Bahcesehir University（巴切塞希尔大学人工智能工程系）；Cancer Systems Biology Lab, Graduate School of Informatics, Middle East Technical University（中东技术大学信息研究生院癌症系统生物学实验室）；Dept. of Pharmaceutical Chemistry, Faculty of Pharmacy, Gazi University（加齐大学药学系药剂化学系）；Laboratory of Molecular Modeling, Evias Pharmaceutical R&D Ltd.（Evias制药研发有限公司分子建模实验室）；Dept. of Electrical and Electronics Engineering, İskenderun Technical University（伊斯肯德伦技术大学电子与电气工程系）；Institute for Computational Biomedicine, Heidelberg University（海德堡大学计算生物医学研究所）；Dept. of Bioinformatics, Graduate School of Health Sciences, Hacettepe University（哈切塔佩大学健康科学研究生院生物信息学系）

Atabey Ünlü, Elif Çevrim, Melih Gökay Yiğit, Ahmet Sarıgün, Hayriye Çelikbilek, Osman Bayram, Deniz Cansen Kahraman, Abdurrahman Olğaç, Ahmet Sureyya Rifaioğlu,… 展开作者

Atabey Ünlü, Elif Çevrim, Melih Gökay Yiğit, Ahmet Sarıgün, Hayriye Çelikbilek, Osman Bayram, Deniz Cansen Kahraman, Abdurrahman Olğaç, Ahmet Sureyya Rifaioğlu, Erden Banoğlu, Tunca Doğan

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AI总结：

提出DrugGEN，一种基于图Transformer生成对抗网络的端到端系统，用于从头设计靶向AKT1蛋白的药物候选分子，经分子对接、动力学及体外实验验证，成功发现两种低微摩尔浓度抑制剂。

摘要：

发现新型药物候选分子是药物开发中最基本和关键的步骤之一。生成式深度学习模型能够根据概率分布创建合成数据，为从头设计分子提供了巨大潜力。然而，要在实际药物开发流程中应用，这些模型需要能够设计具有药物特性且以靶标为中心的分子。在本研究中，我们提出了一种端到端的生成系统DrugGEN，用于从头设计能与目标靶蛋白相互作用的药物候选分子。所提出的方法将分子表示为图，并通过包含图Transformer层的生成对抗网络进行处理。该系统使用大规模药物样化合物和靶标特异性生物活性分子数据集进行训练，以设计针对AKT1蛋白的有效抑制分子，该蛋白在多种癌症治疗开发中至关重要。我们进行了分子对接和动力学模拟以评估模型的靶标中心生成性能，并通过注意力分数可视化检验模型的可解释性。同时，对选定的化合物进行了化学合成，并在体外酶活性测定中进行了评估，鉴定出两种在低微摩尔浓度下抑制AKT1的生物活性分子。这些结果表明，DrugGEN从头设计的分子在与AKT1蛋白相互作用方面具有与其天然配体相当的高潜力。利用开源的DrugGEN代码库，只要给定实验已知生物活性分子的数据集，即可轻松训练针对其他可成药蛋白的模型。

英文摘要：

Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. Generative deep learning models, which create synthetic data given a probability distribution, offer a high potential for designing de novo molecules. However, to be utilisable in real life drug development pipelines, these models should be able to design drug like and target centric molecules. In this study, we propose an end to end generative system, DrugGEN, for the de novo design of drug candidate molecules that interact with intended target proteins. The proposed method represents molecules as graphs and processes them via a generative adversarial network comprising graph transformer layers. The system is trained using a large dataset of drug like compounds and target specific bioactive molecules to design effective inhibitory molecules against the AKT1 protein, which is critically important in developing treatments for various types of cancer. We conducted molecular docking and dynamics to assess the target centric generation performance of the model, as well as attention score visualisation to examine model interpretability. In parallel, selected compounds were chemically synthesised and evaluated in the context of in vitro enzymatic assays, which identified two bioactive molecules that inhibited AKT1 at low micromolar concentrations. These results indicate that DrugGEN's de novo molecules have a high potential for interacting with the AKT1 protein at the level of its native ligands. Using the open access DrugGEN codebase, it is possible to easily train models for other druggable proteins, given a dataset of experimentally known bioactive molecules.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。