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E3. Computer Design of Drugs

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    Given a 3D structure of a protein, a clever synthetic chemist should be able to design a drug to fit into any "pit or canyon" that serves as a binding site for a biological ligand. Virtual reality simulations showing the protein target and drug with feedback on binding interactions provided to hand inputs using a haptic device are being used in drug design.

    • iconexternal_link.gifRoles of Haptics in Teaching Structural Molecular Biology

    Many computer programs exists as well that will "dock" small ligand to active sites on large biological molecules.

    • iconexternal_link.gifAUTODOCK: Automatic Docking of Flexible Ligands to Macromolecules


    • iconexternal_link.gifZinc: A Free Digital Molecular Library Database for structure-matching

    • iconexternal_link.gifBiomolecular Data Base - including BIND

    Drugs are intended to be target-specific; however, many drugs interact with other physiological targets which may result in side effects. In order to understand drug-target associations and predict new targets for established drugs, Keiser M. et. all used a similarity ensemble approach (SEA) to compare 3,665 FDA approved drugs to hundreds of different ligands. In this approach, they compared targets based on the similarity of ligands that bind to the different targets. This comparison predicted 257 drug-target associations that were not noted in either the MDL Drug Data Report or the World of Molecular Bioactivity database. Of these, they then tested 30 of the predictions. 14 of the 30 were found to have 23 previously unknown targets.

    Keiser, then went on to analyze if these new targets were biologically relevant. To do so, they used the following criteria:

    • Did the new targets contribute to the primary activity of the drug,
    • Did they cause side effects, or
    • Were the new targets unrelated by sequence, structure, and function to the typical target.

    When testing whether some of the new targets were primary sites of action, they noted that for some of the different drugs, the binding affinity was higher for the new target than it was for the intended target. For example, Keiser et. all used SEA to compile different ligands for dimethyltryptamine (DMT). Based on this, and DMT's side effects, they tested the binding affinity of DMT to different receptors and found that DMT would bind five of these. All of these receptors had substantially greater affinities for DMT than the intended target. Some of the new off-targets also gave insight into several drugs� side effects. Finally, there were four drugs that did not bind to targets that were related by sequence or structure to their intended targets. However, these new targets were related to the intended targets in that they had similar ligands regulating their activities. Overall, Keiser et. all were able to use the similarity ensemble approach based on ligand similarities to map new off-target associations for drugs. This approach can be used to gain better understanding of drug associations and to predict new molecular targets

    This page titled E3. Computer Design of Drugs is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by Henry Jakubowski.

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