Molecular Dynamics and Artificial Intelligence Based Tools to Innovate Covalent and Noncovalent Drug Design

基于分子动力学和人工智能的工具来创新共价和非共价药物设计

• 批准号: 10323917
• 负责人: Julie Anne Harris
• 金额: $ 70.28万
• 依托单位: COMPUTCHEM LLC
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10323917
• 关键词: Academia; Affinity; Algorithms; Amino Acids; Architecture; Artificial Intelligence; Benchmarking; Binding; Binding Sites; Biophysics; Code; Communicable Diseases; Complex; Computer Assisted; Computer software; Contracts; Cysteine; Data; Data Science; Databases; Descriptor; Drug Design; Drug Kinetics; Future; Goals; Human; Hydrogen; Industry; Knowledge; Letters; Licensing; Ligands; Link; Lysine; Machine Learning; Malignant Neoplasms; Methods; Modeling; Modernization; Molecular; Molecular Conformation; Pharmacologic Substance; Phase; Protac; Proteins; Proteome; Research; Scanning; Scheme; Scientist; Site; Small Business Innovation Research Grant; Software Tools; Solvents; Speed; Testing; Therapeutic; Titrations; Toxic effect; Training; Translating; Validation; Work; base; cloud software; combat; computational chemistry; deep learning; design; drug discovery; experience; frontier; improved; in silico; inhibitor/antagonist; innovation; ionization; kinase inhibitor; lead optimization; molecular dynamics; novel; programs; protein degradation; protein protein interaction; protonation; prototype; simulation; small molecule; tool; virtual screening

Abstract Cancer and infectious diseases are threatening modern human kind. To combat the challenge, targeted cova- lent inhibition and targeted protein degradation are emerging as the new frontiers in drug discovery. This SBIR project seeks to meet two unmet needs in the emerging covalent as well as traditional reversible drug discovery programs by developing two commercial solutions based on cutting-edge research in molecular simulations and data science. In the Phase I period, ComputChem LLC has developed a local/cloud software application iTitrate that can offer accurate and reliable predictions of protein pKa values and nucleophilic cysteine and lysine. As the first of its kind, iTitrate is based on a unique and extensively validated constant pH molecular dynamics approach and a very accurate and efficient generalized Born implicit-solvent model. In Phase I, ComputChem also devel- oped a prototype of a novel small-molecule pKa prediction tool based on artificial intelligence. Building on the Phase I progress, the objective of the Phase II project is to significantly accelerate, enhance, and demonstrate the capabilities of iTitrate and improve the accuracy of iKa. iTitrate can be applied to expedite efforts in a vari- ety of covalent drug discovery programs, including targeted covalent inhibitor design; kinome or proteome wide scanning for covalently targetable sites; protein-protein interaction inhibition; and proteolysis targeting chimera design. iTitrate can be further developed and integrated with iKa to offer a novel solution for accurate protonation state assignment and pH-dependent modeling of protein-ligand complexes, which would add significant value to computer-aided lead optimization and other in silico drug design tasks.

摘要 癌症和传染病正威胁着现代人类。为了应对挑战，有针对性的cova- Lent抑制和靶向蛋白质降解正在成为药物发现的新前沿。该SBIR 该项目旨在满足新兴共价和传统可逆药物发现中两个未满足的需求 通过开发两种基于分子模拟前沿研究的商业解决方案， 数据科学在第一阶段，ComputChem LLC开发了本地/云软件应用程序iTitrate 它可以提供蛋白质pKa值和亲核半胱氨酸和赖氨酸的准确可靠的预测。为 iTitrate是同类产品中的第一款，基于独特且经过广泛验证的恒定pH分子动力学方法 和一个非常精确和有效的广义Born隐式溶剂模型。在第一阶段，ComputChem还开发了- 开发了一种基于阿尔蒂智能的新型小分子pKa预测工具的原型。基础上 第一阶段的进展，第二阶段项目的目标是显着加快，增强，并展示 iTitrate的功能并提高iKa的准确度。iTitrate可用于加快瓦里工作的进度- 共价药物发现计划的一部分，包括靶向共价抑制剂设计;激酶组或蛋白质组范围 扫描共价靶向位点;蛋白质-蛋白质相互作用抑制;和蛋白质水解靶向嵌合体 设计iTitrate可进一步开发并与iKa集成，为精确质子化提供新的解决方案 状态分配和蛋白质-配体复合物的pH依赖性建模，这将为 计算机辅助先导物优化和其他计算机药物设计任务。