A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors.

ABSTRACT: The high variability of the human immunodeficiency virus (HIV) is an important cause of HIV resistance to reverse transcriptase and protease inhibitors. There are many variants of HIV type 1 (HIV-1) that can be used to model sequence-resistance relationships. Machine learning methods are widely and successfully used in new drug discovery. An emerging body of data regarding the interactions of small drug-like molecules with their protein targets provides the possibility of building models on "structure-property" relationships and analyzing the performance of various machine-learning techniques. In our research, we analyze several different types of descriptors in order to predict the resistance of HIV reverse transcriptase and protease to the marketed antiretroviral drugs using the Random F

SUBMITTER: Tarasova O 

PROVIDER: S-EPMC6278491 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature