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molSimplify 2.0:改进结构生成,用于无机分子和网状化学中的自动发现

Gianmarco G Terrones¹, Roland G St Michel^1,2, Jacob W Toney¹

¹Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

Journal of chemical information and modeling

|February 25, 2026

概括

molSimplify软件已更新,用于自动化分子和材料建模. 新功能改进了过渡金属复合物的生成,并使各种材料的高通量de novo设计成为可能.

科学领域:

计算化学的计算化学
材料科学材料科学材料科学
化学信息学化学信息学

背景情况:

自动分子建模需要高效的工具来处理复杂的化学结构.
产生具有高密度的过渡金属复合物 (TMC) 并避免固态碰撞是具有挑战性的.
将建模能力扩展到周期系统和金属酶对于更广泛的应用至关重要.

研究的目的:

提供 molSimplify 的核心功能和最近的增强功能概述.
为分子和材料建模引入新类和改进的算法.
为了证明molSimplify对TMC和其他复杂系统的新生代的实用性.

主要方法:

用于存储和处理原子/结合信息的mol3D和atom3D类的描述.
引入mol2D类用于基于图形的独特性和亚结构识别.
改进了装饰和基层结构的增加,用于系统的分子衍生.
改进过渡金属复合物生成,包括绝缘碰撞消除和高密度联结物处理.
与机器学习模型集成,用于预测协调原子身份.
蛋白3D类用于金属酶建模的应用.
使用工作流来从SMILES字符串中生成Ir复合体的演示.

主要成果:

增强了阅读,修改和表征分子几何学的功能.
通过改进的装饰和基结构功能,对模板分子进行系统的衍生.
成功生成具有更高密度的过渡金属复合物 (TMC) 并且没有绝缘碰撞.
通过机器学习集成实现的高吞吐量,de novo TMC 生产.
在周期系统 (MOF) 和金属酶中已证明的应用.
通过准确生成已知Ir复合体的结构来验证工作流.

结论:

最近对molSimplify的改进显著改善了自动化分子和材料建模.
更新后的代码有助于TMC和相关结构的高吞吐量de novo生成.
molSimplify可以扩展到各种周期性材料,如MOF,COF和石,以及多金属TMC.

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