Predicting HIV-1 Protease and Reverse Transcriptase Drug Resistance Using Fuzzy Cognitive Maps

Several antiviral drugs have been approved for treating HIV infected patients. These drugs inhibit the function of proteins which are essential in the virus life cycle, thus preventing the virus reproduction. However, due to its high mutation rate the HIV is capable to develop resistance to administered therapy. For this reason, it is important to study the resistance mechanisms of the HIV proteins in order to make a better use of existing drugs and design new ones. In the last ten years, numerous statistical and machine learning approaches were applied for predicting drug resistance from protein genome information. In this paper we first review the most relevant techniques reported for addressing this problem. Afterward, we describe a Fuzzy Cognitive Map based modeling which allows representing the causal interactions among the protein positions and their influence on the resistance. Finally, an extended comparison experimentation is carried out, which reveals that this model is competitive with well-known approaches and notably outperforms other techniques from literature.