The applications of machine learning in HIV neutralizing antibodies research—A systematic review

Machine learning algorithms play an essential role in bioinformatics and allow exploring the vast and noisy biological data in unrivaled ways. This paper is a systematic review of the applications of machine learning in the study of HIV neutralizing antibodies. This significant and vast research domain can pave the way to novel treatments and to a vaccine. We selected the relevant papers by investigating the available literature from the Web of Science and PubMed databases in the last decade. The computational methods are applied in neutralization potency prediction, neutralization span prediction against multiple viral strains, antibody–virus binding sites detection, enhanced antibodies design, and the study of the antibody-induced immune response. These methods are viewed from multiple angles spanning data processing, model description, feature selection, evaluation, and sometimes paper comparisons. The algorithms are diverse and include supervised, unsupervised, and generative types. Both classical machine learning and modern deep learning were taken into account. The review ends with our ideas regarding future research directions and challenges. •Antibodies design and antibody-induced immune response are of high interest.•The neutralization potency, span and binding sites of HIV antibodies can be predicted.•Input features are a mix of amino acid sequences, structural and phylogenetic data.•Data processing is very diverse, and sometimes computational simulation was used.•Feature selection helps uncover virus–antibody contact sites (epitopes).