Today’s drug discovery pipelines are under a lot of pressure. Although in the past years (since 2010) the approval rate of the FDA has slightly increased, pharma companies are still searching for ways to make drug discovery more efficient, especially reducing attrition rates later on the clinic. Fail fast, fail cheap is still very well valid!
Therefore, reducing potentially failing NMEs as early and as cheaply as possible has become of paramount importance in the drug discovery process. This includes, but is not limited to, the application of sophisticated software tools to avoid as many experiments as possible.
However, there is a conundrum, as software tools used by chemists are mostly too complicated to use, and feature way more functionality than necessary.
Enter SeeSAR, an easy to use but very sophisticated tool to design molecules in 3D right there, in the active site. SeeSAR will predict, which improvement is indeed an improvement, and which change will likely lead to failures. Now with inclusion of ADME/T properties (from the well-known StarDrop feature by Optibrium), the chemist gets an overview of the most important parameters of the drug discovery process: affinity, LLE, LE, MW, BBB, hERG, log D, log P, solubility etc.
We will lead you through the design process with SeeSAR and show you how you too can make your drug discovery more efficient!