The structure–activity relationship (SAR) matrix (SARM) methodology and data structure was originally developed to extract structurally related compound series from data sets of any composition, organize these series in matrices reminiscent of R-group tables, and visualize SAR patterns. The SARM approach combines the identification of structural relationships between series of active compounds with analog design, which is facilitated by systematically exploring combinations of core structures and substituents that have not been synthesized. The SARM methodology was extended through the introduction of DeepSARM, which added deep learning and generative modeling to target-based analog design by taking compound information from related targets into account to further increase structural novelty. Herein, we present the foundations of the SARM methodology and discuss how DeepSARM modeling can be adapted for the design of compounds with dual-target activity. Generating dual-target compounds represents an equally attractive and challenging task for polypharmacology-oriented drug discovery. The DeepSARM-based approach is illustrated using a computational proof-of-concept application focusing on the design of candidate inhibitors for two prominent anti-cancer targets.

构效关系 (SAR) 矩阵 (SARM) 方法和数据结构最初开发用于从任何组成的数据集中提取结构相关的化合物系列，将这些系列组织在让人联想到 R 组表的矩阵中，并可视化 SAR 模式。SARM 方法将一系列活性化合物之间结构关系的鉴定与类似物设计相结合，这通过系统地探索尚未合成的核心结构和取代基的组合来促进。SARM 方法通过引入 DeepSARM 得到扩展，通过考虑来自相关目标的化合物信息，将深度学习和生成建模添加到基于目标的模拟设计中，以进一步增加结构新颖性。在此处，我们介绍了 SARM 方法的基础，并讨论了如何调整 DeepSARM 建模以设计具有双目标活性的化合物。生成双靶点化合物对于多药理学导向的药物发现来说是一项同样有吸引力且具有挑战性的任务。使用计算概念验证应用程序来说明基于 DeepSARM 的方法，该应用程序侧重于为两个突出的抗癌目标设计候选抑制剂。