Statistical Mechanics with Quantum Potentials: Application to Host-Gues

项目摘要 宿主分子，如环糊精和葫芦醛，可以‘捕获’较小的分子并影响 它们的物理和化学行为。宿主分子与其较小的分子结合越强 “宾客”的影响越大。宿主分子本身也可以被化学改变(即 衍生)，这可以改变它们与客体分子结合的强度，以及它们自己的物理性质 属性。科学家们正在发现许多与人类健康相关的主客体应用 技术，包括改进药物的性能，使其更有效和更安全， 清除化学战剂和清理环境化学品的潜在清除剂 污染物。这一领域的基础研究和应用/工业研发的数量正在扩大 迅速地。给定特定的客体分子(例如，候选药物、化学污染物)的关键片段 研发人员需要的信息是主客体结合亲和力和关联/解离 费率。这个SBIR项目的目的是开发一个软件工具，可以准确地预测这些主人-客人 结合性质(例如结合自由能)。这将允许研发科学家进行 计算实验减少了昂贵和耗时的工作台数量 需要做实验。目前需要开发这样的软件工具，因为 最近一个盲目的测试挑战证明，现有的工具不够准确，无法提供 对研究人员有用的信息。最近的研究表明，预测的准确性可以 通过将量子力学(QM)能量函数与严格的 统计力学。然而，这些概念验证研究尚未转化为可靠的 适用于应用研发的计算工具。因此，该项目将与VeraChem的当前 统计力学软件包(VM2)与广泛使用的量子化学软件包 GamesS，并为实现这一目标的各种计算方案实现驱动程序。在这 提出的混合方法，分子构象的玻尔兹曼分布仍将是 通过彻底的构象搜索生成，就像经典的VM2一样；然而， 构象搜索将不会仅仅依靠分子力学，而是将由更多的 可靠的QM潜力。QM势也将用于熵项，包括处理 非谐效应。将充分利用最近在可靠性方面的显著改进 半经验质量管理(SEQM)，在更高的质量管理水平上有可选的修正。扭亏为盈 通过并行处理和复杂的构象过滤/审查，将加快计算速度 进程。

Project Summary Host molecules, such as cyclodextrins and cucurbiturils, can 'capture' smaller molecules and affect their physical and chemical behavior. The stronger the host molecule holds onto, i.e. binds, its smaller 'guest' the larger the effect can be. Host molecules themselves can also be chemically altered (i.e. derivatized), which can change how strongly they bind guest molecules, as well as their own physical properties. Scientists are discovering many human health-related applications for host-guest technology, including improvement of the properties of drugs to make them more effective and safer, potential scavengers for chemical warfare agent removal, and clean-up of environmental chemical pollutants. The amount of basic research as well as applied/industrial R&D in this area is expanding rapidly. Given a particular 'guest' molecule (e.g. drug candidate, chemical pollutant) key pieces of information R&D scientists require is the host-guest binding affinity and the association/dissociation rates. This SBIR project aims to develop a software tool that can accurately predict these host-guest binding properties (e.g. binding free energy). This would allow R&D scientists to carry out computational experiments reducing the number of expensive and time-consuming bench experiments required. There is a current need for such a software tool to be developed, because as recently demonstrated by a blinded test challenge, existing tools are not accurate enough to provide useful information to researchers. Very recent studies indicate that the accuracy of the predictions can be significantly improved by combining quantum mechanical (QM) energy functions with rigorous statistical mechanics. However, these proof-of-concept studies have yet to be translated into a robust computational tool suitable for applied R&D. Therefore, this project will interface VeraChem's current statistical mechanics software package (VM2) with the widely used quantum chemistry package GAMESS, and implement drivers for various computational schemes to achieve this goal. In this proposed hybrid methodology, a Boltzmann distribution of molecular conformations will still be generated via a thorough conformational search as it is for classical VM2; however, the conformational search will not solely rely on molecular mechanics but will be guided by the more reliable QM potential. QM potentials will also be used for entropy terms, including a treatment of anharmonic effects. Full advantage will be taken of recent dramatic improvements in reliability of semi-empirical QM (SEQM), with optional corrections at higher levels of QM. Turnaround of calculations will be speeded up by parallel processing and a sophisticated conformer filter/vetting process.