Accelerating drug discovery via ML-guided iterative design and optimization

通过机器学习引导的迭代设计和优化加速药物发现

• 批准号: 10552325
• 负责人: David Lowell Mobley
• 金额: $ 41.58万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552325
• 关键词: Acceleration; Active Learning; Affinity; Anti-Bacterial Agents; Area; Automation; Back; Binding; Biological Assay; Communities; Computational Technique; Computer software; Computers; Computing Methodologies; Consumption; Coupled; Coupling; DNA; Databases; Development; Disease; Engineering; Failure; Free Energy; Health Benefit; Investments; Laboratories; Libraries; Ligands; Machine Learning; Medicine; Methods; Modeling; Oral; Output; Pharmacologic Substance; Play; Process; Proteins; Public Health; Recommendation; Research; Research Personnel; Resources; Rewards; Science; Screening Result; Series; Solubility; Structural Biologist; Techniques; Therapeutic; Time; Vision; Work; blind; combinatorial; common treatment; computerized tools; cost; design; drug discovery; experimental study; guided inquiry; improved; interest; iterative design; lead optimization; model design; new technology; novel; novel therapeutics; open data; open source; process optimization; screening; tool

PROJECT SUMMARY/ABSTRACT The Mobley laboratory focuses on developing and using computational tools to dramatically accelerate pharma- ceutical drug discovery. We focus on the interface between methods and applications, and invest in assessing and improving computational methods as well as applying methods directly in discovery. We take an open approach (open science, open source software, open data), making our work a community resource, including our FreeSolv database of solvation free energies, the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) series of blind challenges, our Lead Optimization Mapper (LOMAP) tool for automation of binding calculations, and the Open Force Field and Open Free Energy projects. Tools and methods we have contributed to are now broadly used in drug discovery research, including in pharma. Our overall vision is to make modeling a tool which plays a key role guiding drug discovery research, reducing costs, time and trial and error. In particular, we want researchers – ranging from medicinal chemists to structural biologists as well as experts in computation – to routinely input their latest results and ideas into their computer at the end of the work day, and return to work to find prioritized next steps for their research. For example, in the lead optimization process, one might input the latest assay results as well as ideas for new compounds which could be screened next, and on returning to work in the morning, find ideas ranked by affinity for the target, potential off- target effects and predicted solubility/oral availability. Results might also include additional synthetically accessible compounds not originally considered. If predictions were accurate, this pipeline would dramatically accelerate discovery; thus, we seek to make workflows like this a reality via our science and engineering efforts. In our next five years, we plan to develop an increasingly automated iterative pipeline for iterative library design, compound screening, and optimization. With an experimental partner, we use computation to design promising DNA-encoded compound libraries, computationally analyze screening results, then design models to recommend additional compound rounds for screening and further iterations of the cycle. When combinatorial screening leads to promising enough compounds, we shift to compound optimization, employing active learning in combination with free energy methods and machine learning to prioritize compounds for synthesis and, when possible, for purchase from compound libraries like Enamine, with assay results guiding additional cycles. Results from this work feed back into improving our models and guide early stage drug discovery projects. Our focus involves both pipeline development and actual discovery. While we are developing methods that can be applied to any therapeutic area or target when coupled with experimental work, we will also focus on antibacterial discovery, a particular interest for us and our partners in the Paegel lab. Their novel screening and discovery platform, coupled with our expertise in computational techniques to guide discovery, allow the development of a powerful new platform for pharmaceutical design, our focus for the next few years.

项目总结/摘要 莫布里实验室专注于开发和使用计算工具，以显着加快制药， 药物的发现。我们专注于方法和应用程序之间的接口，并投资于评估和 改进计算方法以及直接在发现中应用方法。我们采取开放的态度 (open科学，开源软件，开放数据），使我们的工作成为社区资源，包括我们的FreeSolv 溶剂化自由能数据库，蛋白质和配体建模的统计评估（SAMPL） 一系列盲目的挑战，我们的铅优化映射器（LOMAP）工具的自动化绑定计算， 开放力场和开放自由能源项目我们贡献的工具和方法现在 广泛用于药物发现研究，包括制药。 我们的总体愿景是使建模成为一种工具，在指导药物发现研究中发挥关键作用， 成本、时间和试错。特别是，我们希望研究人员-从药物化学家到结构 生物学家和计算专家--定期将他们的最新结果和想法输入他们的计算机， 工作日结束后，返回工作岗位，为他们的研究确定优先的下一步。例如，在领导 在优化过程中，人们可以输入最新的测定结果以及可以被应用的新化合物的想法。 第二天早上回到工作岗位时，根据对目标的热爱程度、潜在的偏离程度， 靶向作用和预测的溶解度/口服利用度。结果还可能包括额外的合成访问 最初没有考虑的化合物。如果预测是准确的，这条管道将大大加快 因此，我们寻求通过我们的科学和工程努力使这样的工作流成为现实。 在接下来的五年里，我们计划为迭代库设计开发一个越来越自动化的迭代管道， 化合物筛选和优化。与实验合作伙伴一起，我们使用计算来设计有前途的 DNA编码的化合物库，计算分析筛选结果，然后设计模型以推荐 用于筛选的额外化合物轮次和循环的进一步迭代。当组合筛选导致 对于有前途的化合物，我们转向化合物优化，结合使用主动学习， 利用自由能方法和机器学习来优先合成化合物，并在可能的情况下， 从Enamine等化合物库购买，分析结果指导额外的循环。结果从这个 工作反馈到改进我们的模型和指导早期阶段的药物发现项目。 我们的重点包括管道开发和实际发现。当我们在开发可以 应用于任何治疗领域或目标时，再加上实验工作，我们也将重点放在抗菌 发现，我们和我们的合作伙伴特别感兴趣的是在Escherogel实验室。他们的新筛选和发现 平台，再加上我们在计算技术方面的专业知识，以指导发现，允许开发一个 强大的药物设计新平台，我们未来几年的重点。