|23:00 CST (Shanghai),||16:00 CET (Berlin),||11:00 am EDT (New York)|
Chemspace just renewed its claim to be the largest online resource for small molecules. Including the REAL Space with more than 11 billion virtual product molecules provided on demand by Enamine, Chemspace again outnumbers all other small molecule resources by orders of magnitude. The success of your project depends on choosing the right source of molecules and medicinal chemistry expertise. At Chemspace, we understand your needs and guarantee molecules delivery in the required amount with high purity and always on time. Close collaboration with our respectful suppliers including Enamine, FCH group, and UORSY, among others, allows us to achieve this goal. In this webinar you'll learn what Chemspace offers, how we work and why Chemspace stands out as the premier provider of high-quality drug discovery solutions.
Three personal drug discovery case studies will be presented from the medicinal chemistry perspective:
In many cases the binding site of a ligand with its target is known from experiments before docking approaches are used. In this case, the search space can be limited when looking for therapeutic ligands. As a consequence, the speed of screening is enhanced. In other cases, a therapeutic ligand shows good effects on the target but no or limited structural information of that target is available, especially in the presence of the ligand. Consequently, novel strategies of how to approach this issue with existing software is of need.
Up to nine putative therapeutic ligands are used in various docking protocols, which also includes the application of different docking software, searching for unknown binding sites on the target. The target is the viral channel forming protein (viroporin) p7 of hepatitis C virus. Protein p7 consists of two transmembrane domains separated by a short loop. In its functional form it is shown to exist genotype dependent as a homo hexamer or heptamer. The protein is vital to the survival of the virus.
The discovery of bioactive small molecules is an expensive and time consuming yet central task in drug discovery. A potentially superior way to identify new drug candidates is the selective optimization of side activities (SOSA), which employs known drugs for their side-activities as lead compounds, while diminishing their original activity. Virtually every small molecule drug interacts with more than one molecular target and, thus, has side-activities.
We have observed such side-activity for the cysteinyl leukotriene receptor 1 (CysLT1R) antagonist cinalukast on the nuclear peroxisome proliferator-activated receptor α (PPARα). We chose this synthetically challenging experimental drug to study whether the application of well-established computational optimization and ranking methods can help identify the most promising variations for SOSA and reduce the synthetic efforts needed in this lead optimization concept. We employed SeeSAR to visualize the critical parts for supportive or undesirable interactions with the nuclear receptor. In a proof-of-concept study, we confirmed the suitability of the HYDE ranking for the task of compound prioritization concerning potency on PPARα and screened an automatically generated virtual library of approximately 8000 close cinalukast analogues using a self-designed KNIME® workflow with FlexX and HYDE. The top-ranking molecules from this first aspect of SOSA were then computationally studied for CysLT1R antagonism using a random forest model trained on fingerprint representations of known CysLT1R antagonists. A computationally favoured cinalukast analogue was synthesized and its in vitro profiling confirmed the predicted activity shift towards higher activation efficacy on PPARα and markedly improved selectivity over CysLT1R compared to the lead compound.