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.

项目摘要/摘要 Mobley实验室致力于开发和使用计算工具，以极大地加速药物。 CEUTICAL毒品发现。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。我们采取开放态度 （开放科学，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 溶液自由能的数据库，蛋白质和配体建模的统计评估（Sampl） 一系列盲目挑战，我们的铅优化映射器（LOMAP）工具用于自动化计算， 以及开放部队和开放自由能项目。我们为现在做出的工具和方法 广泛用于药物发现研究，包括制药。 我们的整体愿景是使建模成为指导药物发现研究的关键作用的工具，从而减少了药物发现。 成本，时间和反复试验。特别是，我们希望研究人员 - 从医学化学家到结构 生物学家以及计算专家 - 将其最新结果和想法定期输入到他们的计算机上 工作日结束，并重新开始工作以确定其研究的下一步。例如，领先 优化过程，可能会输入最新的测定结果以及可能是新化合物的想法 接下来放映，并在早上重返工作岗位后，发现因目标而排名的想法 目标效应和预测的溶解度/口服可用性。结果可能还包括其他合成访问 最初不考虑化合物。如果预测准确，该管道将大大加速 发现;因此，我们试图通过我们的科学和工程努力使这样的工作成为现实。 在接下来的五年中，我们计划为迭代库设计开发越来越自动化的迭代管道， 复合筛选和优化。与实验合作伙伴一起，我们使用计算来设计承诺 DNA编码的化合物库，计算分析筛选结果，然后设计模型 其他复合弹以进行筛查和循环的进一步迭代。当组合筛选引线时 为了承诺足够的化合物，我们转向复合优化，结合使用主动学习 使用自由能的方法和机器学习，以优先考虑合成的化合物，并在可能的情况下进行 从Enamine等复合库中购买，并带有指导其他周期的测定结果。结果 工作可以改善改善我们的模型，并指导早期阶段的药物发现项目。 我们的重点涉及管道开发和实际发现。当我们开发可以是 与实验工作相结合时，应用于任何治疗区域或目标，我们还将重点放在抗菌上 发现，对我们和我们在佩杰尔实验室中的合作伙伴的特别兴趣。他们的新颖筛选和发现 平台，再加上我们在指导发现的计算技术方面的专业知识，允许开发 强大的制药设计平台，这是我们未来几年的重点。