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Fragment Based Lead Discovery

In the emerging area of fragment-based drug discovery, the design of the fragment library is of critical importance. Furthermore, the quality of the library has to be monitored before each series of experiments, confirming the ligand's stability and solubility. As the affinity results are collected, a large volume of statistically relevant information is accumulated, and it is critical to analyse the trends, and store it in an easily accessible fashion, for use in future projects.

NMR, SPR, CE, DSF, X-ray, and Biochemical assays are often used to detect the weak affinity binding typical in fragment-based lead discovery (FBLD), with NMR often used in large scale screening, followed by complementary techniques. The data produced is numerous, making analysis of the data very time consuming. ACD/Labs software for analytical data interpretation and chemical and analytical knowledge management assists in preserving this body of information, readily available for the next research and development study. Furthermore, the software can offer simplification, improved visualization, and automation of the manual and routine steps.

Visualization and Analysis of NMR Ligand Binding Data

Several different methods for ligand-based NMR screening have been employed in different research groups. Some NMR experiments include Saturation Transfer Difference (STD), WaterLOGSY, TINS, and T2-based screening amongst others. Existing workflows for analysing NMR ligand binding data involve manually identifying bound interactions from protein NMR spectra and comparing them against a library of spectra from fragments. ACD/Labs software and customized scripting solutions can help to:

Assistance with Lead Design

After ligands affinity is identified, the lead candidate is designed and tested, with provisions for future bioavailability. Using industry standard property predictions, ACD/Structure Design Engine software interactively suggests structural modifications of lead compounds, outside of the pharmacophore, to improve physicochemical, ADMET, and Toxicity endpoints.

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