A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds,Journal of Chemical Information and Modeling

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A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2018-06-28 00:00:00 , DOI: 10.1021/acs.jcim.7b00475
Thomas M. Kaiser ₁ , Pieter B. Burger _{1,

2} , Christopher J. Butch _{1,

3} , Stephen C. Pelly ₁ , Dennis C. Liotta ₁

Affiliation

HIV resistance emerging against antiretroviral drugs represents a great threat to the continued prolongation of the lifespans of HIV-infected patients. Therefore, methods capable of predicting resistance susceptibility in the development of compounds are in great need. By targeting the major reverse transcription residues Y181, K103, and L100, we used the biological activities of compounds against these enzymes and the wild-type reverse transcriptase to create Naïve Bayes Networks. Through this machine learning approach, we could predict, with high accuracy, whether a compound would be susceptible to a loss of potency due to resistance. Also, we could perfectly predict retrospectively whether compounds would be susceptible to both a K103 mutant RT and a Y181 mutant RT. In the study presented here, our method outperformed a traditional molecular mechanics approach. This method should be of broad interest beyond drug discovery efforts, and serves to expand the utility of machine learning for the prediction of physical, chemical, or biological properties using the vast information available in the literature.

中文翻译：

一种预测生物活性化合物的HIV逆转录酶突变敏感性的机器学习方法

对抗逆转录病毒药物产生的艾滋病毒抗药性对继续感染艾滋病毒的患者的寿命构成了巨大威胁。因此，迫切需要能够预测化合物开发中的抗药性的方法。通过靶向主要的逆转录残基Y181，K103和L100，我们利用化合物针对这些酶和野生型逆转录酶的生物活性来创建NaïveBayes网络。通过这种机器学习方法，我们可以高精度地预测化合物是否会由于抗性而失去效力。同样，我们可以完美地回顾性地预测化合物是否对K103突变体RT和Y181突变体RT均敏感。在这里提出的研究中，我们的方法优于传统的分子力学方法。除了药物发现以外，这种方法应该引起广泛的兴趣，并使用文献中提供的大量信息来扩展机器学习的效用，以预测物理，化学或生物学特性。

更新日期：2018-06-28

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