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

项目摘要/摘要 这项工作旨在推进生物分子设计的定量方法，特别是预测生物分子 互动，通过一系列有针对性的社区盲目预测挑战。预测结合的物理方法 自由能或“自由能法”有望戏剧性地重塑早期的药物发现。 已经在fi和制药铅优化中的应用。然而，性能是不可靠的， 适用范围有限，制药应用中的失败通常很难理解和 fix。另一方面，这些方法现在通常可以预测各种简单的物理属性，例如 溶剂化自由能或相对溶解度，尽管在精确度方面仍有明显的改进空间。在……里面 近年来，竞争和众包已被证明是推动不同领域创新的有效模式 fi字段。在我们的fi时代，盲预测挑战在推动物理预测方面的创新方面发挥了关键作用 属性和绑定，特别是在SAMPL形式的一系列挑战。在这里，我们将继续和 扩展SAMPL预测挑战，以包括新的物理属性、更复杂的主机-客户绑定 数据以及在生物分子系统中的应用。精心挑选的系统和新颖的实验数据将提供 在系统之间逐渐增加复杂性的挑战，这些系统现在容易处理 略微超出了今天的方法，但仍然比药物设计中涵盖的方法略简单 数据资源(D3R)对现有医药数据的一系列挑战。我们将与D3R合作，实现盲目运行 对我们生成的数据的挑战，并确保其设计为最大限度地有利于fi和fi现场。 在目标1中，我们将收集关于类药物化合物的分配、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AIM 2中，我们利用我们在主宾方方面的专业知识 结合以生成关于葫芦和深腔空洞中主-客体结合的新数据。在《目标3》中，我们 使用高通量机器人实验来生成具有生物相关性的新的蛋白质-配体结合数据。目标 4专注于在Sampl系列挑战中使用这些数据，应用基于众包的成熟技术 推动新方法的发展和对现有方法的优点和缺点的新认识 技巧。我们还将使用最新的技术进行参考计算。 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 fi领域的创新，并成为100种不同出版物的焦点(每种出版物通常被引用5-50次) 大约在2007年左右开始，并将在推动未来几代人在 分子设计的计算技术。这里提出的研究将导致显著的fiCan改进 在药物发现、分子设计和物理模型预测方面的预测能力 属性。

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.