The compound optimization monitor (COMO) approach was originally developed as a diagnostic approach to aid in evaluating development stages of analog series and progress made during lead optimization. COMO uses virtual analog populations for the assessment of chemical saturation of analog series and has been further developed to bridge between optimization diagnostics and compound design. Herein, we discuss key methodological features of COMO in its scientific context and present a deep learning extension of COMO for generative molecular design, leading to the introduction of DeepCOMO. Applications on exemplary analog series are reported to illustrate the entire DeepCOMO repertoire, ranging from chemical saturation and structure–activity relationship progression diagnostics to the evaluation of different analog design strategies and prioritization of virtual candidates for optimization efforts, taking into account the development stage of individual analog series.

化合物优化监测 (COMO) 方法最初是作为一种诊断方法开发的，旨在帮助评估模拟系列的开发阶段以及先导化合物优化过程中取得的进展。 COMO 使用虚拟类似物群体来评估类似物系列的化学饱和度，并经过进一步开发以在优化诊断和化合物设计之间架起桥梁。在此，我们在科学背景下讨论了 COMO 的关键方法学特征，并提出了 COMO 用于生成分子设计的深度学习扩展，从而引入了 DeepCOMO。据报道，示例性模拟系列的应用说明了整个 DeepCOMO 的全部功能，从化学饱和度和结构-活性关系进展诊断到不同模拟设计策略的评估以及优化工作的虚拟候选者的优先级，同时考虑到个体的开发阶段模拟系列。