当前的工作旨在设计并提供选择性BMX抑制剂的初步IND启动研究，以用于癌症治疗的开发。BMX是一种新兴的靶点，尤其在肿瘤和免疫性疾病中更为突出。在这项工作中，我们采用了基于预测人工智能平台来设计选择性抑制剂，考虑了新颖性、知识产权保护和药物相似性等性质。此外，我们还合成并化学表征了最初设计的几个优选候选化合物，使用1H-NMR和LC-MS进行了表征。我们利用一系列生化（酶）和癌细胞系列，对所选分子进行了这些试验。此外，我们还利用人工智能来预测和评估所选分子的几个重要的与IND相关的理化性质和药代动力学值。当前工作的第二个目标也是验证BMX在已知由BMX介导的动物模型中的独特作用。在本研究中，设计了50多个分子，所采用的是五种新发现的支架。有两个分子被提名进行进一步的IND相关研究。化合物II在酶活性与其他激酶相比以及在已知BMX过表达的癌细胞系列中显示出有前景的体外活性。有趣的是，化合物II的理化性质和药代动力学性质也非常有利，正如所用平台预测的那样。动物研究进一步确认了BMX在疾病模型中的独特作用。当前工作为选择性BMX抑制剂作为潜在的治疗开发领头化合物提供了有希望的数据，并且该资源目前正处于优化阶段。值得注意的是，当前研究展示了一个结合了人工智能和实验的综合方法的框架，学术实验室可以在其研究计划中使用该方法，以便更顺利地将项目转为以IND为重点的，以便与工业合作伙伴进行进一步的临床开发。版权所有©2023 Elsevier B.V.保留所有权利。

The current work aims to design and provide a preliminary IND-enabling study of selective BMX inhibitors for cancer therapeutics development. BMX is an emerging target, more notably in oncological and immunological diseases. In this work, we have employed a predictive AI-based platform to design the selective inhibitors considering the novelty, IP prior protection, and drug-likeness properties. Furthermore, selected top candidates from the initial iteration of the design were synthesized and chemically characterized utilizing 1H-NMR and LC-MS. Employing a panel of biochemical (enzymatic) and cancer cell lines, the selected molecules were tested against these assays. In addition, we used artificial intelligence to predict and evaluate several critical IND-focused physicochemical and pharmacokinetics values of the selected molecules. A secondary objective of the current work was also to validate the sole role of BMX in animal models known to be mediated by BMX. More than 50 molecules were designed in the present study employing five novel discovered scaffolds. Two molecules were nominated for further IND-focused studies. Compound II showed promising in-vitro activity against BMX in both enzymatic assays compared to other kinases and in cancer cell lines with known BMX overexpression. Interestingly, compound II showed very favorable physicochemical and pharmacokinetics properties as predicted by the used platforms. The animal study further confirmed the sole role of BMX in the disease model. The current work provides promising data on a selective BMX inhibitor as a potential lead for therapeutics development, and the asset is currently in the optimization stage. Notably, the current study shows a framework for a combined approach employing both AI and experimentation that can be used by academic labs in their research programs to more streamline programs into IND-focused to be bridged easily for further clinical development with industrial partners.Copyright © 2023 Elsevier B.V. All rights reserved.