Solanki Priyanka, Abdul Amin Sk, Manhas Anu
Department of Chemistry, Pandit Deendayal Energy University, Gandhinagar, 382426, India.
Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, Fisciano, 84084, SA, Italy; Department of Pharmaceutical Technology, JIS University, 81, Nilgunj Road, Agarpara, Kolkata, 700109, West Bengal, India.
Comput Biol Med. 2025 Sep;196(Pt A):110712. doi: 10.1016/j.compbiomed.2025.110712. Epub 2025 Jul 10.
Cyclin-dependent kinase 2 (CDK2) modulates the progression of the cell cycle, and its dysregulation results in unchecked cellular proliferation, establishing it as a pivotal target in oncological therapies. We implemented a comprehensive screening pipeline to identify potential novel inhibitors for the CDK2 enzyme by integrating advanced machine learning classification methods. The random forest (RF) method shows better performance based on the statistical metrics assessment. This RF model was used to filter a large coconut dataset comprising 477,975 molecules to identify potential candidates. This initial screening process identified 327 candidate molecules. The subsequent application of PAINS (Pan-Assay Interference Structures) filtration refined this list to 309 molecules, which were then selected for molecular docking analysis. Based on the docking score, the top 40 potential candidates from molecular docking analysis were chosen for pharmacokinetics (PK) and pharmacodynamics (PD) studies (ADMET). Three molecules that satisfy the PK/PD criteria were selected for DFT and molecular dynamics simulation studies. The finalized three molecules displayed conserved interactions with the residues Lys33 and Asp145, crucial for enzyme inhibition. Moreover, Molecule 2 possessed an extended fused heterocyclic system, which may enhance its inhibitory potential. The simulation studies indicate that these compounds showed stable behavior within the binding pocket of the CDK2 enzyme. Also, we have developed an open-access online tool named "pCDK2i_v1.0" to help the scientific community efficiently screen the potential CDK2 inhibitors. This work demonstrates the importance of integrating machine learning in drug design to discover novel anti-cancer inhibitors of the CDK2 enzyme. The pCDK2i_v1.0 tool for screening and predicting the CDK2 activity as active (1), and inactive (0) is available at https://github.com/Amincheminfom/pCDK2i_v1.
细胞周期蛋白依赖性激酶2（CDK2）调节细胞周期进程，其失调会导致细胞增殖失控，使其成为肿瘤治疗中的关键靶点。我们实施了一个综合筛选流程，通过整合先进的机器学习分类方法来识别CDK2酶的潜在新型抑制剂。基于统计指标评估，随机森林（RF）方法表现出更好的性能。该RF模型用于筛选包含477,975个分子的大型椰子数据集，以识别潜在的候选分子。这个初步筛选过程确定了327个候选分子。随后应用PAINS（泛分析干扰结构）过滤将该列表精简至309个分子，然后选择这些分子进行分子对接分析。根据对接分数，从分子对接分析中选出前40个潜在候选分子进行药代动力学（PK）和药效学（PD）研究（ADMET）。选择了三个符合PK/PD标准的分子进行密度泛函理论（DFT）和分子动力学模拟研究。最终确定的三个分子与对酶抑制至关重要的赖氨酸33（Lys33）和天冬氨酸145（Asp145）残基表现出保守的相互作用。此外，分子2拥有一个扩展的稠合杂环系统，这可能增强其抑制潜力。模拟研究表明，这些化合物在CDK2酶的结合口袋内表现出稳定的行为。此外，我们还开发了一个名为“pCDK2i_v1.0”的开放获取在线工具，以帮助科学界高效筛选潜在的CDK2抑制剂。这项工作证明了在药物设计中整合机器学习以发现CDK2酶新型抗癌抑制剂的重要性。用于筛选和预测CDK2活性为活性（1）和非活性（0）的pCDK2i_v1.0工具可在https://github.com/Amincheminfom/pCDK2i_v1获取。
