会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 8. 发明申请
    • SYSTEM AND METHOD FOR SYSTEMATIC PREDICTION OF LIGAND/RECEPTOR ACTIVITY
    • 用于系统预测配体/受体活性的系统和方法
    • WO2002072613A1
    • 2002-09-19
    • PCT/SG2001/000049
    • 2001-03-10
    • KENT RIDGE DIGITAL LABSBRUSIC, Vladimir
    • BRUSIC, Vladimir
    • C07K1/00
    • G06F19/16G06F19/22
    • Disclosed is a general system and method, for prediction of binding of peptide-like ligands (peptides) to peptide-like receptors (receptors). Specifically this invention uses non-linear prediction models (including, but not limited to, artificial neural networks), sequence data form ligands and their respective receptors, and known ligand-receptor binding affinities. The representation of ligand-receptor interaction used along with the binding affinity of said interaction is used to train a determining means in a form of a predictive model. Prediction of binding affinity of a novel (not used for training of a predictive model) ligand-receptor interaction, involving a peptide and a particular receptor, involves the combining of representations of both peptide and receptor and presenting that representation to a previously trained predictive model. The system and method can be used as a single predictive model for determination of ligand binding to an individual receptor, or to a group of related receptors. This system and method was validated using data on peptide binding to major histocompatibility complex molecules (MHC) and artificial neural networks (ANN).
    • 公开了用于预测肽样配体(肽)与肽样受体(受体)的结合的一般系统和方法。 具体地,本发明使用非线性预测模型(包括但不限于人造神经网络),序列数据形式配体及其各自的受体以及已知的配体 - 受体结合亲和力。 使用的配体 - 受体相互作用的表示以及所述相互作用的结合亲和力被用于以预测模型的形式训练确定手段。 涉及肽和特定受体的新型(不用于预测模型的训练)配体 - 受体相互作用的结合亲和力的预测包括将肽和受体的表达组合并将该表示呈现给先前训练的预测模型 。 该系统和方法可以用作单个预测模型,用于测定配体与单个受体或一组相关受体的结合。 使用关于肽与主要组织相容性复合分子(MHC)和人工神经网络(ANN)结合的数据验证了该系统和方法。