Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit.
Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit.
复制标题
使用 RDKit 的 DOCK6 描述符驱动的从头设计算法。
DOI：
10.1021/acs.jcim.3c01031
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发表时间：
2023
影响因子：
5.6
通讯作者：
Rizzo,RobertC
中科院分区：
文献类型：
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作者：
DuarteRamosMatos,Guilherme;Pak,Steven;Rizzo,RobertC
采用从头设计原理的基于结构的方法可用于使用预先存在的片段库从零开始构建小有机分子以采样化学空间，并且是用于药物先导发现的一类重要的计算算法。在这里，我们提出了一个强大的新的设计方法DOCK 6，采用描述符驱动的从头策略（称为D3 N），其中用户定义的化学信息学描述符（及其目标范围）计算在每一层的增长使用开源工具包RDKit。目标是使配体生长朝向化学空间的期望区域。该方法通过以下方式进行了广泛验证：（1）使用新的DOCK 6/RDKit接口与标准Python/RDKit安装计算的化学信息学描述符的比较，（2）检查在不同条件下（目标范围和环境）使用D3 N生长生成的描述符分布，以及（3）使用临床相关化合物作为参考构建具有非常紧密（精确）描述符范围的配体。我们的测试证实了新的DOCK 6/RDKit集成是强大的，展示了新的D3 N例程如何用于围绕用户定义的化学空间直接采样，并强调了实时描述符计算在重要药物靶点配体设计中的实用性。
Structure-based methods that employ principles of de novo design can be used to construct small organic molecules from scratch using pre-existing fragment libraries to sample chemical space and are an important class of computational algorithms for drug-lead discovery. Here, we present a powerful new design method for DOCK6 that employs a Descriptor-Driven De Novo strategy (termed D3N) in which user-defined cheminformatics descriptors (and their target ranges) are calculated at each layer of growth using the open-source toolkit RDKit. The objective is to tailor ligand growth toward desirable regions of chemical space. The approach was extensively validated through: (1) comparison of cheminformatics descriptors computed using the new DOCK6/RDKit interface versus the standard Python/RDKit installation, (2) examination of descriptor distributions generated using D3N growth under different conditions (target ranges and environments), and (3) construction of ligands with very tight (pinpoint) descriptor ranges using clinically relevant compounds as a reference. Our testing confirms that the new DOCK6/RDKit integration is robust, showcases how the new D3N routines can be used to direct sampling around user-defined chemical spaces, and highlights the utility of on-the-fly descriptor calculations for ligand design to important drug targets.