Liu Xiaohong, Zhang Wei, Tong Xiaochu, Zhong Feisheng, Li Zhaojun, Xiong Zhaoping, Xiong Jiacheng, Wu Xiaolong, Fu Zunyun, Tan Xiaoqin, Liu Zhiguo, Zhang Sulin, Jiang Hualiang, Li Xutong, Zheng Mingyue
Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
J Cheminform. 2023 Apr 8;15(1):42. doi: 10.1186/s13321-023-00711-1.
Artificial intelligence (AI)-based molecular design methods, especially deep generative models for generating novel molecule structures, have gratified our imagination to explore unknown chemical space without relying on brute-force exploration. However, whether designed by AI or human experts, the molecules need to be accessibly synthesized and biologically evaluated, and the trial-and-error process remains a resources-intensive endeavor. Therefore, AI-based drug design methods face a major challenge of how to prioritize the molecular structures with potential for subsequent drug development. This study indicates that common filtering approaches based on traditional screening metrics fail to differentiate AI-designed molecules. To address this issue, we propose a novel molecular filtering method, MolFilterGAN, based on a progressively augmented generative adversarial network. Comparative analysis shows that MolFilterGAN outperforms conventional screening approaches based on drug-likeness or synthetic ability metrics. Retrospective analysis of AI-designed discoidin domain receptor 1 (DDR1) inhibitors shows that MolFilterGAN significantly increases the efficiency of molecular triaging. Further evaluation of MolFilterGAN on eight external ligand sets suggests that MolFilterGAN is useful in triaging or enriching bioactive compounds across a wide range of target types. These results highlighted the importance of MolFilterGAN in evaluating molecules integrally and further accelerating molecular discovery especially combined with advanced AI generative models.
基于人工智能（AI）的分子设计方法，尤其是用于生成新型分子结构的深度生成模型，激发了我们在不依赖蛮力探索的情况下探索未知化学空间的想象力。然而，无论分子是由人工智能还是人类专家设计，都需要能够进行合成并进行生物学评估，而反复试验的过程仍然是一项资源密集型的工作。因此，基于人工智能的药物设计方法面临着一个重大挑战，即如何对具有后续药物开发潜力的分子结构进行优先级排序。这项研究表明，基于传统筛选指标的常见过滤方法无法区分人工智能设计的分子。为了解决这个问题，我们提出了一种基于渐进增强生成对抗网络的新型分子过滤方法MolFilterGAN。比较分析表明，MolFilterGAN优于基于类药性或合成能力指标的传统筛选方法。对人工智能设计的盘状结构域受体1（DDR1）抑制剂的回顾性分析表明，MolFilterGAN显著提高了分子筛选的效率。对八个外部配体集上的MolFilterGAN进行的进一步评估表明，MolFilterGAN在对广泛的靶标类型的生物活性化合物进行筛选或富集方面很有用。这些结果突出了MolFilterGAN在整体评估分子以及进一步加速分子发现方面的重要性，特别是与先进的人工智能生成模型相结合时。
