Advancing predictive physical modeling through focused development of model systems to drive new modeling innovations

项目总结/摘要 本工作旨在促进生物分子设计的定量方法，特别是用于预测生物分子 互动，通过一系列集中的社区盲预测挑战。预测结合的物理方法 自由能量，或“自由能量方法”，正准备大大重塑早期药物发现， 已经在制药铅优化中找到了应用。但是，性能不可靠， 适用范围有限，药物应用中的失败往往难以理解， fix.另一方面，这些方法现在通常可以预测各种简单的物理性质， 溶剂化自由能或相对溶解度，尽管在精确度上仍有明显的改进空间。在 近年来，竞争和众包已被证明是推动多样化创新的有效模式。 菲尔德。在我们的领域，盲预测挑战在推动物理预测创新方面发挥了关键作用。 属性和约束力，特别是在SAMPL的形式一系列的挑战。在这里，我们将继续， 扩展SAMPL预测挑战，包括新的物理性质，更复杂的主客体结合 数据和应用于生物分子系统。精心挑选的系统和新颖的实验数据将提供 逐渐增加的复杂性的挑战，跨越系统之间，现在是易于处理的那些， 略超出了今天的方法，但仍然比药物设计所涵盖的方法略简单 数据资源（D3 R）对现有制药数据的一系列挑战。我们将与D3 R合作， 挑战我们生成的数据，并确保它的设计是为了最大限度地贝内该领域。 在目标1中，我们将收集关于药物样化合物的分配、分布和质子化的新测量结果， 与制药行业的合作伙伴合作。在目标2中，我们利用我们在主客体关系方面的专业知识， 结合产生新的数据，在葫芦脲和深腔cavitands的主客体结合。在目标3中， 使用高通量机器人实验来产生新的生物相关的蛋白质-配体结合数据。目的 4侧重于在SAMPL系列挑战中使用这些数据，应用经过验证的基于众包的技术 推动新方法的发展，并对现有方法的优点和缺点有新的认识， 技术.我们还将使用最新技术运行参考计算。 这项工作将确保SAMPL挑战的持续成功，这些挑战已经推动了相当大的 在该领域的创新，并成为100个不同的出版物的焦点（每个通常被引用5-50次），因为 他们在2007年左右成立，并将在推动未来几代的改进中发挥关键作用， 分子设计的计算技术。这里提出的研究将导致显着的改善 在药物发现、分子设计和物理模型预测方面， 特性.

PROJECT SUMMARY / ABSTRACT This work seeks to advance quantitative methods for biomolecular design, especially for predicting biomolecular interactions, via a focused series of community blind prediction challenges. Physical methods for predicting binding free energies, or “free energy methods”, are poised to dramatically reshape early stage drug discovery, and are already finding applications in pharmaceutical lead optimization. However, performance is unreliable, the domain of applicability is limited, and failures in pharmaceutical applications are often hard to understand and fix. On the other hand, these methods can now typically predict a variety of simple physical properties such as solvation free energies or relative solubilities, though there is still clear room for improvement in accuracy. In recent years, competitions and crowdsourcing have proven an effective model for driving innovations in diverse fields. In our field, blind prediction challenges have played a key role in driving innovations in prediction of physical properties and binding, especially in the form of the SAMPL series of challenges. Here, we will continue and extend SAMPL prediction challenges to include new physical properties, more complicated host-guest binding data, and application to biomolecular systems. Carefully selected systems and novel experimental data will provide challenges of gradually increasing complexity spanning between systems which are now tractable to those which are marginally out of reach of today's methods but still slightly simpler than those covered by the Drug Design Data Resource (D3R) series of challenges on existing pharmaceutical data. We will work with D3R to run blind challenges on the data we generate and to ensure it is designed to maximally benefit the field. In Aim 1, we will collect new measurements on partitioning, distribution, and protonation of drug-like compounds, in collaboration with partners in the pharmaceutical industry. In Aim 2, we leverage our expertise in host-guest binding to generate new data on host-guest binding in cucubiturils and deep cavity cavitands. And in Aim 3, we use high-throughput robotic experiments to generate new protein-ligand binding data of biological relevance. Aim 4 focuses on using this data in the SAMPL series of challenges, applying proven crowdsourcing-based techniques to drive the development of new methods and new understanding of the strengths and weaknesses of existing techniques. We will also run reference calculations with the latest techniques. This work will ensure the continued success of SAMPL challenges which have already driven considerable innovation in the field and been the focus of 100 different publications (each typically cited 5-50 times) since their inception around 2007, and will play a key role in driving the next several generations of improvements in computational techniques for molecular design. The research proposed here will lead to significant improvements in the predictive power of physical models for drug discovery, molecular design and the prediction of physical properties.