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Machine-learning scoring functions have consistently been found to outperform classical scoring functions at binding affinity prediction of diverse protein-ligand complexes. [17] [18] This has also been the case for target-specific complexes, [19] [20] although the advantage is target-dependent and mainly depends on the volume of relevant data ...
GDoQ: GDoQ (Prediction of GLMU inhibitors using QSAR and AutoDock) is an open source platform for predicting inhibitors against Mycobacterium tuberculosis (M.Tb) drug target N-acetylglucosamine-1-phosphate uridyltransferase (GLMU) protein. This is a potential drug target involved in bacterial cell wall synthesis.
An example drug affinity responsive target stability (DARTS) workflow. Binding of a drug to a protein often leads to ligand-induced stabilization of the protein. In DARTS, drug and control treated proteins are subjected to limited proteolysis and the extent of protein digestion can either be visualized on a gel or measured by mass spectrometry .
The phrase "drug design" is similar to ligand design (i.e., design of a molecule that will bind tightly to its target). [6] Although design techniques for prediction of binding affinity are reasonably successful, there are many other properties, such as bioavailability, metabolic half-life, and side effects, that first must be optimized before a ligand can become a safe and effictive drug.
This approach is known as "reverse pharmacology" or "target based drug discovery" (TDD). [5] However recent statistical analysis reveals that a disproportionate number of first-in-class drugs with novel mechanisms of action come from phenotypic screening [6] which has led to a resurgence of interest in this method. [1] [7] [8]
high affinity towards the target (less than 1 μM) selectivity versus other targets; significant efficacy in a cellular assay; druglikeness (moderate molecular weight and lipophilicity usually estimated as ClogP). Affinity, molecular weight and lipophilicity can be linked in single parameter such as ligand efficiency and lipophilic efficiency.
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For example, imagine a plate wherein compound A is in wells 1–2–3, compound B is in wells 2–3–4, and compound C is in wells 3–4–5. In an assay of this plate against a given target, a hit in wells 2, 3, and 4 would indicate that compound B is the most likely agent, while also providing three measurements of compound B's efficacy ...