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, have been developed with the purpose of picking completely new samples from a partially known space. Generative models offer high potential for designing de novo molecules; however, in order for them to be useful in real-life drug development pipelines, these models should be able to design target-specific molecules, which is the next step in this field. In this study, we propose DrugGEN, for the de novo design of drug candidate molecules that interact with selected target proteins. The proposed system represents compounds and protein structures as graphs and processes them via serially connected two generative adversarial networks comprising graph transformers. DrugGEN is trained using a large dataset of compounds from ChEMBL and target-specific bioactive molecules, to design effective and specific inhibitory molecules against the AKT1 protein, which has critical importance for developing treatments against various types of cancer. On fundamental benchmarks, DrugGEN models have either competitive or better performance against other methods. To assess the target-specific generation performance, we conducted further in silico analysis with molecular docking and deep learning-based bioactivity prediction. Results indicate that de novo molecules have high potential for interacting with the AKT1 protein structure in the level of its native ligand. DrugGEN can be used to design completely novel and effective target-specific drug candidate molecules for any druggable protein, given target features and a dataset of experimental bioactivities. Code base, datasets, results and trained models of DrugGEN are available at https://github.com/HUBioDataLab/DrugGEN

翻译：发现新型药物候选分子是药物开发中最基础且关键的步骤之一。生成式深度学习模型能够根据概率分布创建合成数据，其开发目的是从部分已知空间中选取全新的样本。生成模型在从头设计分子方面具有巨大潜力；然而，要使其在真实的药物开发流程中发挥作用，这些模型必须能够设计靶向特异性分子——这正是该领域的下一步研究方向。在本研究中，我们提出DrugGEN，用于设计与选定靶蛋白相互作用的药物候选分子。该系统将化合物和蛋白质结构表示为图，并通过串联两个由图Transformer组成的生成对抗网络进行处理。DrugGEN利用ChEMBL的大规模化合物数据集和靶向特异性生物活性分子进行训练，以设计针对AKT1蛋白（该蛋白对开发多种癌症治疗药物具有关键意义）的有效且特异性抑制分子。在基础基准测试中，DrugGEN模型相比其他方法表现出具有竞争力或更优的性能。为评估靶向特异性生成能力，我们通过分子对接和基于深度学习的生物活性预测进行了进一步的计算分析。结果表明，从头设计的分子在与其天然配体水平上具有与AKT1蛋白结构相互作用的巨大潜力。给定靶标特征和实验生物活性数据集，DrugGEN可用于设计针对任何可成药蛋白的全新且有效的靶向特异性药物候选分子。DrugGEN的代码库、数据集、结果和训练模型可在https://github.com/HUBioDataLab/DrugGEN 获取。