Improving empirical force fields: a big-data approach.

改善经验力场：大数据方法。

• 批准号: DP180101421
• 负责人: Prof Alan Mark
• 金额: $ 24.3万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2018
• 资助国家: 澳大利亚
• 起止时间: 2018-01-01 至 2021-07-01
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP180101421
• 关键词: Improving; empirical; force; fields; big

This project aims to improve the ability to represent the thermodynamic properties of molecules of biological, pharmaceutical or materials interest by developing force fields capable of describing a diverse range of molecules both consistently and with high fidelity. The project aims to exploit a rapidly expanding, in-house database of parameterized molecular structures to develop highly optimised, well-validated parameters that are both consistent and transferable, enabling molecules of any size or complexity to be parameterised with a fidelity currently only possible for simple organics. This will provide significant benefits, such as helping to improve the accuracy and reliability of ligand: protein complexes determined experimentally, a limiting factor in computational drug design.

该项目旨在通过开发能够一致和高保真地描述各种分子的力场来提高代表生物，药物或材料感兴趣的分子的热力学性质的能力。该项目旨在利用一个快速扩展的内部参数化分子结构数据库，开发高度优化、经过充分验证的参数，这些参数既一致又可转移，使任何大小或复杂性的分子都能以目前仅适用于简单有机物的保真度进行参数化。这将提供显着的好处，如帮助提高配体的准确性和可靠性：蛋白质复合物确定实验，在计算药物设计的限制因素。