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

通过集中开发模型系统来推进预测物理建模，以推动新的建模创新

基本信息

批准号：
10000168
负责人：
David Lowell Mobley
金额：
$ 34.91万
依托单位：
UNIVERSITY OF CALIFORNIA-IRVINE
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-10 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10000168
关键词：
Accounting Affect Affinity Area Automobile Driving Back Binding Binding Proteins Biological Biological Models Cell Membrane Permeability Chemicals Collaborations Communities Computational Biology Computational Technique Computing Methodologies Data Data Set Development Disease Docking Drug Design Drug Industry Drug Targeting Ensure Evaluation Failure Fostering Free Energy Funding Generations Head Health Human Individual Informatics Learning Ligands Measurement Measures Methodology Methods Modeling Modification Molecular Performance Pharmaceutical Preparations Pharmacologic Substance Phase Play Proteins Publications Research Robotics Running Sampling Science Series Serum Albumin Solubility Solvents Stress Tests System Techniques Technology Testing Time Treatment Factor United States National Institutes of Health Work aqueous base blind catalyst cavitand crowdsourcing data resource design drug development drug discovery experimental study foot improved innovation insight lead optimization model development molecular recognition new technology novel personalized medicine physical model physical property predictive tools protonation receptor small molecule small molecule therapeutics success targeted treatment tautomer virtual screening

项目摘要

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.

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，特别是用于预测生物分子互动，通过一系列集中的社区盲目预测挑战。预测绑定的物理方法自由能或“自由能法”被中毒以重塑早期药物发现，并且已经在药物铅优化中找到了应用。但是，性能是不可靠的适用性领域是有限的，药品应用中的失败通常很难理解和 x。另一方面，这些方法现在通常可以预测各种简单的物理特性，例如解决方案自由能或相对溶解度，尽管仍然有明确的准确性空间。在近年来，竞争和众包被证明是推动潜水员创新的有效模型领域。在我们的领域，盲目的预测挑战在推动物理预测的创新方面发挥了关键作用属性和结合，尤其是在Sampl系列挑战的形式中。在这里，我们将继续扩展样本预测挑战，包括新的物理特性，更复杂的宿主 - 环绑定数据和应用于生物分子系统。精心选择的系统和新颖的实验数据将提供系统之间逐渐增加复杂性的挑战，这些系统现在可以解决这些问题今天的方法遥不可及，但仍然比药物设计所涵盖的方法略简单数据资源（D3R）对现有药物数据的挑战系列。我们将与D3R合作以盲目我们生成的数据面临的挑战，并确保其设计为最大的利益。在AIM 1中，我们将收集有关类似药物的化合物的分配，分布和质子化的新测量结果，与制药行业合作伙伴合作。在AIM 2中，我们利用我们的专业知识结合以生成有关库孔贝氏和深腔腔中宿主 - 圈结合的新数据。在AIM 3中，我们使用高通量机器人实验生成生物学相关性的新蛋白质结合数据。目的 4专注于在一系列挑战中使用这些数据，应用基于众包的技术技术推动新方法的发展以及对现有的优势和劣势的新理解技术。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，这些挑战已经驱动了很大在该领域的创新，是100个不同出版物的重点（通常引用了5-50次），因为他们的成立于2007年，将在推动接下来的几代改进方面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重大改进在药物发现，分子设计和物理预测的物理模型的预测能力中特性。