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Binding-Site Modeling with Multiple-Instance Machine-Learning

使用多实例机器学习的结合位点建模

基本信息

批准号：
9904662
负责人：
AJAY N JAIN
金额：
$ 31.02万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-01-01 至 2021-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9904662
关键词：
3-Dimensional Address Affinity Behavior Binding Binding Proteins Binding Sites Biological Biological Assay Characteristics Charge Chemicals Computer Assisted Computer software Computing Methodologies Data Data Set Development Docking Drug Design Drug Industry Electrostatics Formulation Future Goals Hydrogen Bonding Industrialization Industry Collaboration Knowledge Laboratories Ligand Binding Ligands Machine Learning Measurement Methods Modeling Modernization Molecular Molecular Probes Performance Pharmaceutical Chemistry Pharmaceutical Preparations Physics Positioning Attribute Procedures Protein Conformation Proteins Research Series Structural Models Surface Testing Variant Work base blind combinatorial design drug discovery improved interest lead optimization machine learning method method development novel novel strategies physical model pre-clinical predictive modeling scaffold segregation small molecule statistical and machine learning targeted treatment tool treatment fees virtual

项目摘要

Project Summary / Abstract This proposal is entitled “Binding-Site Modeling with Multiple-Instance Machine-Learning.” A number of in- terrelated computational methods for making predictions about the biological behavior of small molecules have been the subject of development within the Jain Laboratory for over twenty years. These share a common strat- egy that considers molecular interactions at their surface interface, where proteins and ligands actually interact. These methods yield measurements of similarity between small molecules or between protein binding pockets. They also yield measurements of the complementarity of a small molecule to a protein binding site (the molecular docking problem). A generalization of these concepts makes possible the construction of a virtual binding site for quantitative activity prediction purely from data about the biological activities of a set of small molecules. The goals of the proposed work include further improving the accuracy and breadth of applicability of the binding site modeling approach. The primary application of the approach is to guide optimization of leads within medicinal chemistry projects, and to quantify potential off-target effects during pre-clinical drug discovery. A critical focus of the work will be in data and software dissemination, in order to accelerate the efficient development of targeted therapies. In addition to methods development, the proposed work will involve broad application of these state-of-the-art predictive modeling methods. The proposed work will proceed with the col- laborative input of our pharmaceutical industry colleagues, who have specialized knowledge and data sets that are vital for cutting-edge work in computer-aided drug design. The expected results include more efficient lead optimization (fewer compounds to reach desired biological pa- rameters), truly effective scaffold replacement (to move away from a molecular series with biological limitations), and improved computational predictions of off-target effects during pre-clinical drug design.

项目总结/摘要该提案的标题为“多实例机器学习的绑定站点建模”。一些在- 预测小分子生物学行为的相互关联的计算方法，这是耆那教实验室二十多年来的研究课题。他们有一个共同的战略-- 它考虑了分子在表面界面的相互作用，蛋白质和配体实际上在那里相互作用。这些方法可以测量小分子之间或蛋白质结合口袋之间的相似性。它们还产生小分子与蛋白质结合位点（分子结合位点）的互补性的测量结果。对接问题）。这些概念的概括使得构建用于免疫球蛋白的虚拟结合位点成为可能。纯粹从一组小分子的生物活性数据进行定量活性预测。拟议工作的目标包括进一步提高结合位点建模方法。该方法的主要应用是指导内部的引线优化药物化学项目，并在临床前药物发现过程中量化潜在的脱靶效应。工作的一个关键重点将是数据和软件传播，以加快效率，靶向疗法的开发。除了方法开发，拟议的工作将涉及广泛的这些最先进的预测建模方法的应用。拟议的工作将继续进行，我们的制药行业同事的实验投入，他们拥有专业知识和数据集，对于计算机辅助药物设计的前沿工作至关重要。预期的结果包括更有效的先导物优化（更少的化合物达到所需的生物学参数），纳米），真正有效的支架替代（远离具有生物学限制的分子系列），以及在临床前药物设计期间改进脱靶效应的计算预测。