培训视频—如何更准确的预测药物分子与蛋白的亲和力

川大-灰太狼

BioMS和分迪科技联合德国著名药物设计公司BioSolveIT推出新年免费在线培训（2）
http://bioms.org/forum.php?mod=viewthread&tid=1817


Predicting binding affinity doesn't work – or does it?
预测药物分子与蛋白的亲和力不管用了？如何更准确的预测亲和力？

Predicting binding affinity is still regarded as the holy grail. With SeeSAR,however, we are one step closer to accurately and reliably predict a protein-ligand's binding affinity. What one often neglects is the accuracy of the data, and that, especially in brute force correlation analysis, we might compare apples with oranges.
药物分子与蛋白的亲和力预测一直以来作为评价结合好坏的圣杯。SeeSAR的出现，让我们能更精准的预测药物分子与蛋白的亲和力，SeeSAR直接预测出了药物分子的活性值，让预测更接近真实值。以往我们常常忽视数据的准确性，特别是经常使用野蛮的相关性分析，如我们把苹果和橘子拿来比较。

SeeSAR will tell you where the problems are. So you can understand, without looking at any numbers, where you might have to optimize or where a crystal structure might not be credible. Because they are not, as Derek Lowe once put it, a "message fromGod", but full of assumptions themselves. If we start to understand incorporate this in our work, we will see calculated binding affinity in a whole new (green) light.
SeeSAR将告诉您，您设计的药物分子问题出在了哪里。不用看任何数据，您就会知道药物分子哪里可以优化，晶体复合物的结构中哪里存在着不可靠之处，您该选择哪个晶体复合物作为设计药物分子的起点。正如Derek Lowe说过的，晶体复合物解析出的结构也并不是上帝给我们的信号，也仅仅是试验者的假设。一旦我们理解了SeeSAR的优势，并将其应用在工作中，我们将看到它在药物分子与蛋白的亲和力预测上展现的亮点。


免费培训的视频已经出来，现在发布如下：

maoboo

培训那天刚好有事，报名了没参加，以为错过了，正好学习下！

川大-灰太狼

maoboo 发表于 2015-7-4 17:32
培训那天刚好有事，报名了没参加，以为错过了，正好学习下！

欢迎观看学习：）

WLQ

youku现在的广告真多啊

xufund

能放到百度盘里分享吗？