图 书 馆
灵活的定量构效关系：功能机计算化学学习Flexible QSAR: functional machine learning in computational chemistry |
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| 课程网址: | http://videolectures.net/ecmlpkdd2010_belda_ip/ |
| 主讲教师: | Ignasi Belda |
| 开课单位: | 智能制药公司 |
| 开课时间: | 2010-11-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | QSAR (Quantitative Structure-Activity Relationship) modelling is a usual step in drug discovery. QSAR methods use statistical and machine learning tools to draw out the significant relationships between the molecular structure of the drug candidates (the molecules) and its biological profile. To achieve such a goal, researchers usually describe the molecules with arrays of physico-chemical properties, such as total molecular charge, molecular weight, number of hydrogen bonds donors, etc. However, the predictive accuracy of statistical and machine learning tools in QSAR have been typically very low and more advanced tools are needed to achieve higher degrees of usage of QSAR drug discovery processes. For such a reason, at Intelligent Pharma, we have been researching in the field of functional data mining, that is, data mining of information described through functions, not only with fixed properties. By using functional data mining approaches, we can deal with physico-chemical parameters such as the volume of the molecules, which is a variable property that varies depending on the energy of the system and its flexibility. Therefore, more accurate predictive models can be built by using these approaches in the field of QSAR and drug discovery. The machine learning tool used in this research is support vector machines. |
| 关 键 词: | 定量结构活性关系; 候选药物的分子结构; 功能描述数据挖掘 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-27：zyk |
| 阅读次数: | 63 |