Within the realm of contemporary medicinal chemistry, bioisosteres are empirically used to enhance potency and selectivity, improve adsorption, distribution, metabolism, excretion and toxicity profiles of drug candidates. It is believed that bioisosteric know-how may help bypass granted patents or generate novel intellectual property for commercialization. Beside the synthetic expertise, the drug discovery process also depends on efficient in silico tools. We hereby present BioisoIdentifier (BII), a web server aiming to uncover bioisosteric information for specific fragment. Using the Protein Data Bank as source, and specific substructures that the user attempt to surrogate as input, BII tries to find suitable fragments that fit well within the local protein active site. BII is a powerful computational tool that offers the ligand design ideas for bioisosteric replacing. For the validation of BII, catechol is conceived as model fragment attempted to be replaced, and many ideas are successfully offered. These outputs are hierarchically grouped according to structural similarity, and clustered based on unsupervised machine learning algorithms. In summary, we constructed a user-friendly interface to enable the viewing of top-ranking molecules for further experimental exploration. This makes BII a highly valuable tool for drug discovery. The BII web server is freely available to researchers and can be accessed at http://www.aifordrugs.cn/index/ . Scientific Contribution: By designing a more optimal computational process for mining bioisosteric replacements from the publicly accessible PDB database, then deployed on a web server for throughly free access for researchers. Additionally, machine learning methods are applied to cluster the bioisosteric replacements searched by the platform, making a scientific contribution to facilitate chemists’ selection of appropriate bioisosteric replacements. The number of bioisosteric replacements obtained using BII is significantly larger than the currently available platforms, which expanding the search space for effective local structural replacements.

中文翻译：

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BioisoIdentifier：一个在线免费工具，用于研究 PDB 中的局部结构替换

在当代药物化学领域，生物等排体根据经验用于增强效力和选择性，改善候选药物的吸附、分布、代谢、排泄和毒性特征。据信，生物电子等排技术可能有助于绕过已授予的专利或产生用于商业化的新颖知识产权。除了合成专业知识之外，药物发现过程还取决于高效的计算机工具。我们特此提出 BioisoIdentifier (BII)，这是一个旨在揭示特定片段的生物电子等排信息的网络服务器。使用蛋白质数据库作为来源，并使用用户尝试替代的特定子结构作为输入，BII 尝试找到适合本地蛋白质活性位点的合适片段。BII 是一种强大的计算工具，为生物电子等排替换提供配体设计思路。为了验证BII，邻苯二酚被设想为模型片段，试图被替代，并成功地提供了许多想法。这些输出根据结构相似性进行分层分组，并根据无监督机器学习算法进行聚类。总之，我们构建了一个用户友好的界面，可以查看排名靠前的分子，以进行进一步的实验探索。这使得 BII 成为药物发现的非常有价值的工具。BII网络服务器免费提供给研究人员，可以通过http://www.aifordrugs.cn/index/访问。科学贡献：通过设计一个更优化的计算过程，从可公开访问的 PDB 数据库中挖掘生物电子等排替代品，然后部署在网络服务器上，供研究人员完全免费访问。此外，应用机器学习方法对平台搜索到的生物电子等排替代物进行聚类，为促进化学家选择合适的生物等排替代物做出科学贡献。使用 BII 获得的生物电子等排替换数量明显大于当前可用的平台，这扩大了有效局部结构替换的搜索空间。