Xu Chao, Liu Runduo, Huang Shuheng, Li Wenchao, Li Zhe, Luo Hai-Bin
Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou 570228, Hainan, P.R. China.
School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510000, Guangdong, P.R. China.
Brief Bioinform. 2023 Sep 22;24(6). doi: 10.1093/bib/bbad327.
In the process of drug discovery, one of the key problems is how to improve the biological activity and ADMET properties starting from a specific structure, which is also called structural optimization. Based on a starting scaffold, the use of deep generative model to generate molecules with desired drug-like properties will provide a powerful tool to accelerate the structural optimization process. However, the existing generative models remain challenging in extracting molecular features efficiently in 3D space to generate drug-like 3D molecules. Moreover, most of the existing ADMET prediction models made predictions of different properties through a single model, which can result in reduced prediction accuracy on some datasets. To effectively generate molecules from a specific scaffold and provide basis for the structural optimization, the 3D-SMGE (3-Dimensional Scaffold-based Molecular Generation and Evaluation) work consisting of molecular generation and prediction of ADMET properties is presented. For the molecular generation, we proposed 3D-SMG, a novel deep generative model for the end-to-end design of 3D molecules. In the 3D-SMG model, we designed the cross-aggregated continuous-filter convolution (ca-cfconv), which is used to achieve efficient and low-cost 3D spatial feature extraction while ensuring the invariance of atomic space rotation. 3D-SMG was proved to generate valid, unique and novel molecules with high drug-likeness. Besides, the proposed data-adaptive multi-model ADMET prediction method outperformed or maintained the best evaluation metrics on 24 out of 27 ADMET benchmark datasets. 3D-SMGE is anticipated to emerge as a powerful tool for hit-to-lead structural optimizations and accelerate the drug discovery process.
在药物发现过程中，关键问题之一是如何从特定结构出发提高生物活性和药物代谢动力学性质（ADMET），这也被称为结构优化。基于起始骨架，使用深度生成模型来生成具有所需类药物性质的分子将为加速结构优化过程提供强大工具。然而，现有的生成模型在有效提取三维空间中的分子特征以生成类药物三维分子方面仍具有挑战性。此外，大多数现有的ADMET预测模型通过单个模型对不同性质进行预测，这可能导致在某些数据集上预测准确性降低。为了从特定骨架有效地生成分子并为结构优化提供依据，我们提出了由分子生成和ADMET性质预测组成的3D-SMGE（基于三维骨架的分子生成与评估）方法。对于分子生成，我们提出了3D-SMG，这是一种用于三维分子端到端设计的新型深度生成模型。在3D-SMG模型中，我们设计了交叉聚合连续滤波卷积（ca-cfconv），用于在确保原子空间旋转不变性的同时实现高效低成本的三维空间特征提取。3D-SMG被证明能够生成具有高类药物性的有效、独特和新颖的分子。此外，所提出的数据自适应多模型ADMET预测方法在27个ADMET基准数据集中的24个上优于或保持了最佳评估指标。3D-SMGE有望成为从苗头化合物到先导化合物结构优化的强大工具，并加速药物发现过程。
