SeeSAR is a 21st century molecular modeling software for the medicinal chemist, which helps save time in a plethora of drug discovery applications: exploring proteins, finding binding sites, placing ligands in binding sites, ideation, optimizing ligands in binding sites, improving affinity and ADME/T properties, circumnavigating difficult cores and many more.
This webinar takes you on a tour of some of SeeSAR's many use cases, and will show you how you can apply it to save time, and, ultimately, valuable resources.
In a joint venture, Enamine and BioSolveIT built the world's largest chemical space and made it ultra-fast searchable. The new product, called REAL Space Navigator, comprising 650 million compounds, allows for efficient hit exploration, from finding previously unknown analogues to scaffold hopping. The chemical space encoded with more than 100 Enamine synthesis protocols and in-stock building blocks, provides an escape from availability bias of current stock screening collections towards IP free areas. Compounds selected from this space will be synthesized in 3-4 weeks with an exceptional success rate of 80% and above.
In this webinar we explain the genesis and composition of the space as well as the search technology to access this vast resource. We also demonstrate the user-friendly graphical interface. Afterwards you may simply download → install & execute to explore this huge resource for free within less than 5 minutes. Join us to learn more about this exciting endeavor. Over half a billion virtual molecules that become real on demand are a resource that is simply too valuable to miss...
In collaboration with Zealand Pharma, a leading and experienced therapeutic peptide specialist in Copenhagen, we conceived a tool with the major focus of having it rolled-out and operational ASAP, targeting the most relevant problems and most ugly time-eaters first thus boosting the efficiency of the organization. After approx. 2 years, we can now roll out the tool in its current status to the general public, and academics can use it for free.
PepSee sports, amongst others:
With the availability of more and more protein structures, structure-based design has become a key technology within the early phases of drug design. Protein structures are the only means by which a truly rational design approach gets in sight. While modeling techniques like docking and scoring dealing with small series of protein structures are well established, our methodologies to explore the wealth of information hidden in large collections of protein structures are still rather limited. Most search engines on protein structures are based on text rather than on structural elements, and the analysis of protein structure still requires labor-intense manual steps.
In this talk, new technologies will be presented addressing this opportunity to learn from large structure collections [see doi.org/10.1093/nar/gkx333 and doi.org/10.1016/j.jbiotec.2017.06.004]. On the one hand, the automation of structure preprocessing in the context of drug design plays a crucial role in exploiting large amounts of structural data. On the other hand, search methods allowing to perform geometric queries to structures enable knowledge-driven design decisions. Several examples ranging from interaction geometry analysis, molecular flexibility analysis to design by analogy will be presented.