Development of computational techniques for predicting the free energetics of crystalline polymorphs and complex molecules

纽约大学的马克·塔克曼（Mark Tuckerman）获得了化学学部化学理论、模型和计算方法项目的奖励，以开发理论和计算方法来解决确定准确自由能的关键挑战。(1)为了确定自由能，需要对系统可能的空间构型的复杂的、极高维的概率分布进行广泛采样。(2)尽管系统具有极高的维数，但构象自由能通常可以用几个关键变量来表征；然而，先验地识别这些变量是非常重要的。(3)精确的自由能要求系统中原子间相互作用的精确而计算效率高的模型。塔克曼和他的研究小组通过开发新的计算技术来解决这些挑战，并将这些方法应用于生物分子结构预测和晶体多态性测定中的许多问题。本提案由材料研究部凝聚态物质和材料理论项目共同资助。理论和计算在化学、材料科学和生物学等科学学科中的重要性现在得到了充分的认识，事实上，最近白宫关于材料基因组计划的文件强调了这一点，该文件呼吁理论和实验科学家以及工业工程师之间的协同作用。首要目标是加快从概念到可销售材料之间的时间。理论和计算的作用将通过发展新的数学方法来解决这些领域的突出挑战而得到显著增强。本提案中开发的高效计算协议允许进行快速可靠的预测，可以通过预先筛选形成不希望的多态性的化合物来加速新型药物的开发，并加快生物分子结构的确定。这些信息不仅可以引导涉及新化合物的新药物设计策略，而且可以为这些分子在健康和不健康的细胞环境中如何发挥作用提供重要线索，这将增加我们对某些类型疾病的理解，从而导致新的治疗目标。

Mark Tuckerman of New York University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry division to develop theoretical and computational methods to address key challenges in determining accurate free energies. (1) In order to determine free energies, it is necessary to sample extensively the complex and extremely high-dimensional probability distribution of possible spatial configurations of the system. (2) Despite the extremely high dimensionality of a system, conformational free energies can often be characterized in terms of a few key variables; however, identifying such variables a priori is highly nontrivial. (3) Accurate free energies require an accurate yet computationally efficient model of the interactions between the atoms in a system. Tuckerman and his research group address these challenges through the development of novel computational techniques and apply these methods to a number of problems in biomolecular structure prediction and crystal polymorphism determination. This proposal is cofunded by the Condensed Matter and Materials Theory Program in the Division of Materials Research.The importance of theory and computation in scientific disciplines such as chemistry, materials science, and biology is now well recognized and, in fact, was recently highlighted in the White House's document on the Materials Genome Initiative, which called for a synergy between theoretical and experimental scientists and industrial engineers. The overarching goal is to accelerate the time between concept and a marketable material. The role of theory and computation will be significantly enhanced through the development of new mathematical approaches that address outstanding challenges in these areas. Efficient computational protocols of the type to be developed in this proposal allow rapid and reliable predictions to be made that could accelerate the development of novel pharmaceuticals by pre-screening compounds that form undesirable polymorphs and speed the determination of structure in biomolecules. This information could not only lead to new drug design strategies involving novel compounds, but could provide important clues about how such molecules function in healthy and unhealthy cellular environments, which will increase our understanding of how certain types of diseases, leading to new therapy targets.