Enhancing Drug Discovery Research by Free Energy Modeling

项目摘要/摘要 精确的计算结合自由能模型，估计蛋白质的解离常数- 通过表示化合物及其细胞靶标在原子上的运动来表示配体络合物 解决方案越来越多地促进药物的发现。然而，由于它们的射程有限 适用性，感知的进入壁垒，以及缺乏足够的计算模型 为了更好地利用它们，蛋白质-药物结合的自由能模型仍然不是很广泛 被采纳的，尤指在学术环境中。该项目满足了扩大访问的需求，构建 专业知识，并为更广泛的药物发现池扩展结合自由能计算模型 在学术和工业环境中的应用。它这样做的同时，扩大了研究能力， 布鲁克林学院通过为健康实验项目提供分子建模支持 并为我们的学生提供有意义的教育和研究机会。 PI的实验室已经开发出炼金术转移方法(ATM)来计算结合自由能 融合了最好的科学和下一代 算法，并绕过了传统方法的许多缺点。ATM支持多种 配位体库，并将扩展到处理桥水分子，金属离子和构象 配体结合引起的变化，结合自由能的所有关键和突出问题领域 模特们。通过与当地学术实验室的合作，该项目将研究病毒、癌症和 这些新技术使药物成瘾成为可能。 分子建模工具的好坏取决于它们对分子的洞察能力 疾病的基础。在与实验实验室建立坚实的伙伴关系的基础上，该项目将促进 分子建模在生物化学和药物发现研究中的互补应用 跨学科的学生研究经验。它将建立在成功的iCompute基础上 布鲁克林学院的跨学科本科生研究计划将带来计算和 实验实验室聚在一起，培养下一代研究人员。

PROJECT SUMMARY/ABSTRACT Accurate computational binding free energy models that estimate the dissociation constants of protein- ligand complexes by representing the motion of compounds and their cellular targets at atomic resolution are increasingly contributing to the discovery of drugs. However, due to their limited range of applicability, perceived barriers to entry, and scarcity of computational modelers with sufficient expertise to make good use of them, free energy models of protein-drug binding are still not widely adopted, especially in academic settings. The project addresses the need to widen access, build expertise, and extend binding free energy computational models for a broader pool of drug discovery applications in academic and industrial settings. It does so while expanding the research capacity at Brooklyn College by providing molecular modeling support to experimental projects in the health sciences and offering meaningful educational and research opportunities to our students. The PI’s lab has developed the Alchemical Transfer Method (ATM) for binding free energy estimation that incorporates the best science and simplifications afforded by the next generation of algorithms and circumvents many of the shortcomings of traditional approaches. ATM supports diverse ligand libraries and will be extended to treat bridging water molecules, metal ions, and conformational changes induced by ligand binding, all critical and outstanding problem areas for binding free energy models. Through partnerships with local academic labs, the project will investigate viral, cancer, and drug addiction targets enabled by these novel technologies. Molecular modeling tools are only as good as their ability to give insights into the molecular basis of disease. Building on solid partnerships with experimental laboratories, the project will promote the complementary use of molecular modeling in biochemical and drug discovery research through interdisciplinary student research experiences. It will build upon the successful ICompute Interdisciplinary Undergraduate Research initiative at Brooklyn College to bring computational and experimental laboratories together and nurture the next generation of researchers.