4,6-取代-1,3,5-三嗪-2(1H)-酮是有前景的人类 DNA 拓扑异构酶 IIα 抑制剂。为了进一步开发针对酶 ATP 结合位点的这一化学类别，对 triazin-2(1H)-one 取代位置 6 进行了优化。受临床前取代的 9H-嘌呤衍生物结合的启发，在 6 位处引入了双环取代基，并通过分子模拟、动态药效团和自由能计算的组合验证了这种修饰的实用性。还考虑到 Deepfrag（一种基于深度学习的基于结构的先导化合物优化而开发的软件）的预测，合成了具有双环和单环取代的化合物并研究了其抑制活性。 SAR 数据表明，双环取代化合物对拓扑异构酶 IIα 表现出良好的抑制作用，与​​单取代化合物相当。对一组人类蛋白激酶的进一步评估显示了对拓扑 IIα 的选择性抑制。机理研究表明，这些化合物主要充当催化抑制剂，其中一些在较高浓度下表现出拓扑 IIα 毒害作用。 STD NMR 实验和分子模拟的整合，提供了对结合模型的深入了解，并强调了 Asn120 相互作用以及与位置 4 和 6 取代基的疏水相互作用的重要性。此外，NCI-60 筛选证明了具有双环取代基和确定了敏感的人类癌细胞系，强调了我们的研究结果对于此类化合物进一步临床前开发的转化相关性。该研究强调了模拟和基于人工智能的方法在有效指导药物优化的分子设计方面的协同作用，这对此类化合物的进一步临床前开发具有影响。© 2024 作者。

The 4,6-substituted-1,3,5-triazin-2(1H)-ones are promising inhibitors of human DNA topoisomerase IIα. To further develop this chemical class targeting the enzyme´s ATP binding site, the triazin-2(1H)-one substitution position 6 was optimized. Inspired by binding of preclinical substituted 9H-purine derivative, bicyclic substituents were incorporated at position 6 and the utility of this modification was validated by a combination of molecular simulations, dynamic pharmacophores, and free energy calculations. Considering also predictions of Deepfrag, a software developed for structure-based lead optimization based on deep learning, compounds with both bicyclic and monocyclic substitutions were synthesized and investigated for their inhibitory activity. The SAR data showed that the bicyclic substituted compounds exhibited good inhibition of topo IIα, comparable to their mono-substituted counterparts. Further evaluation on a panel of human protein kinases showed selectivity for the inhibition of topo IIα. Mechanistic studies indicated that the compounds acted predominantly as catalytic inhibitors, with some exhibiting topo IIα poison effects at higher concentrations. Integration of STD NMR experiments and molecular simulations, provided insights into the binding model and highlighted the importance of the Asn120 interaction and hydrophobic interactions with substituents at positions 4 and 6. In addition, NCI-60 screening demonstrated cytotoxicity of the compounds with bicyclic substituents and identified sensitive human cancer cell lines, underlining the translational relevance of our findings for further preclinical development of this class of compounds. The study highlights the synergy between simulation and AI-based approaches in efficiently guiding molecular design for drug optimization, which has implications for further preclinical development of this class of compounds.© 2024 The Authors.