Novak J, Grishina MA, Potemkin VA, Gasteiger J (2020)
Publication Type: Journal article
Publication year: 2020
Book Volume: 12
Pages Range: 299-309
Journal Issue: 4
Aim: This letter investigates the role of radial distribution function-based descriptors for in silico design of new drugs. Methodology: The multiple linear regression models for HIV-1 protease and its complexes with a series of inhibitors were constructed. A detailed analysis of major atomic contributions to the radial distribution function descriptor weighted by the number of valence shell electrons identified residues Arg8, Asp29 and residues of the catalytic triad as crucial for the correlation with the inhibition constant, together with residues Asp30 and Ile50, whose mutations are known to cause an emergence of drug resistant variants. Conclusion: This study demonstrates an easy and fast assessment of the activity of potential drugs and the derivation of structural information of their complexes with the receptor or enzyme.
APA:
Novak, J., Grishina, M.A., Potemkin, V.A., & Gasteiger, J. (2020). Performance of radial distribution function-based descriptors in the chemoinformatic studies of HIV-1 protease. Future Medicinal Chemistry, 12(4), 299-309. https://dx.doi.org/10.4155/fmc-2019-0241
MLA:
Novak, Jurica, et al. "Performance of radial distribution function-based descriptors in the chemoinformatic studies of HIV-1 protease." Future Medicinal Chemistry 12.4 (2020): 299-309.
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