Discovering Dually Active Anti-cancer Compounds with a Hybrid AI-structure-based Approach,Journal of Chemical Information and Modeling

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Discovering Dually Active Anti-cancer Compounds with a Hybrid AI-structure-based Approach
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2024-09-14 , DOI: 10.1021/acs.jcim.4c01132
Michele Roggia ₁ , Benito Natale ₁ , Giorgio Amendola ₁ , Nicola Grasso ₂ , Salvatore Di Maro ₁ , Sabrina Taliani ₃ , Sabrina Castellano ₄ , Serena Concetta Rita Reina ₅ , Erica Salvati ₅ , Jussara Amato ₂ , Sandro Cosconati ₁

Affiliation

Cancer’s persistent growth often relies on its ability to maintain telomere length and tolerate the accumulation of DNA damage. This study explores a computational approach to identify compounds that can simultaneously target both G-quadruplex (G4) structures and poly(ADP-ribose) polymerase (PARP)1 enzyme, offering a potential multipronged attack on cancer cells. We employed a hybrid virtual screening (VS) protocol, combining the power of machine learning with traditional structure-based methods. PyRMD, our AI-powered tool, was first used to analyze vast chemical libraries and to identify potential PARP1 inhibitors based on known bioactivity data. Subsequently, a structure-based VS approach selected compounds from these identified inhibitors for their G4 stabilization potential. This two-step process yielded 50 promising candidates, which were then experimentally validated for their ability to inhibit PARP1 and stabilize G4 structures. Ultimately, four lead compounds emerged as promising candidates with the desired dual activity and demonstrated antiproliferative effects against specific cancer cell lines. This study highlights the potential of combining Artificial Intelligence and structure-based methods for the discovery of multitarget anticancer compounds, offering a valuable approach for future drug development efforts.

中文翻译：

使用基于 Hybrid AI 结构的方法发现双重活性抗癌化合物

癌症的持续生长通常取决于其维持端粒长度和耐受 DNA 损伤积累的能力。本研究探索了一种计算方法来鉴定可以同时靶向 G-四链体（G4）结构和聚（ADP-核糖）聚合酶（PARP）1 酶的化合物，从而对癌细胞进行潜在的多管齐下攻击。我们采用了混合虚拟筛选（VS）协议，将机器学习的强大功能与传统的基于结构的方法相结合。PyRMD 是我们的 AI 工具，最初用于分析庞大的化学库，并根据已知的生物活性数据识别潜在的 PARP1 抑制剂。随后，基于结构的 VS 方法从这些已鉴定的抑制剂中选择化合物，以实现其 G4 稳定潜力。这个两步过程产生了 50 个有前途的候选药物，然后通过实验验证了它们抑制 PARP1 和稳定 G4 结构的能力。最终，四种先导化合物成为具有所需双活性的有前途的候选化合物，并显示出对特定癌细胞系的抗增殖作用。本研究强调了将人工智能和基于结构的方法相结合以发现多靶点抗癌化合物的潜力，为未来的药物开发工作提供了有价值的方法。

更新日期：2024-09-19

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