An interpretable machine learning approach to identify mechanism of action of antibiotics.
An interpretable machine learning approach to identify mechanism of action of antibiotics.
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DOI：
10.1038/s41598-022-14229-3
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发表时间：
2022-06-20
影响因子：
4.6
通讯作者：
Mohimani, Hosein
中科院分区：
文献类型：
--
作者：
Mongia, Mihir;Guler, Mustafa;Mohimani, Hosein
由于抗生素耐药性正在成为世界范围内的主要公共卫生问题，新抗生素发现的方法之一是重新利用市场上可用于治疗抗生素耐药性细菌的药物。这种方法的主要经济优势是，由于这些药物已经通过了所有的安全性测试，它大大降低了临床试验的总成本。最近，已经开发了几种机器学习方法，通过对一组小分子上收集的生物活性数据进行训练来预测有前途的抗生素。然而，这些方法报道了数百/数千种生物活性分子，并且仍然不清楚这些分子中的哪些具有新的作用机制。虽然近年来高通量生物活性测试的成本已大幅下降，但确定小分子的作用机制仍然是一个昂贵且耗时的步骤，因此需要用于优先考虑具有新作用机制的分子的计算方法。用于预测小分子生物活性的现有方法是基于不可解释的机器学习，因此不能确定小分子的已知作用机制并优先考虑新机制。我们介绍InterPred，一种用于预测小分子生物活性及其作用机制的可解释技术。InterPred在生物活性预测方面具有与最新技术水平相同的准确性，并且它能够分配负责生物活性的化学部分。在分析了来自Community for Open Antimicrobial Drug Discovery和美国食品和药物协会批准的药物库的数千种分子对细菌和真菌病原体的生物活性数据后，InterPred确定了部分和作用机制之间的五个已知联系。
As antibiotic resistance is becoming a major public health problem worldwide, one of the approaches for novel antibiotic discovery is re-purposing drugs available on the market for treating antibiotic resistant bacteria. The main economic advantage of this approach is that since these drugs have already passed all the safety tests, it vastly reduces the overall cost of clinical trials. Recently, several machine learning approaches have been developed for predicting promising antibiotics by training on bioactivity data collected on a set of small molecules. However, these methods report hundreds/thousands of bioactive molecules, and it remains unclear which of these molecules possess a novel mechanism of action. While the cost of high-throughput bioactivity testing has dropped dramatically in recent years, determining the mechanism of action of small molecules remains a costly and time-consuming step, and therefore computational methods for prioritizing molecules with novel mechanisms of action are needed. The existing approaches for predicting bioactivity of small molecules are based on uninterpretable machine learning, and therefore are not capable of determining known mechanism of action of small molecules and prioritizing novel mechanisms. We introduce InterPred, an interpretable technique for predicting bioactivity of small molecules and their mechanism of action. InterPred has the same accuracy as the state of the art in bioactivity prediction, and it enables assigning chemical moieties that are responsible for bioactivity. After analyzing bioactivity data of several thousand molecules against bacterial and fungal pathogens available from Community for Open Antimicrobial Drug Discovery and a US Food and Drug Association-approved drug library, InterPred identified five known links between moieties and mechanism of action.
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影响因子：
14.9
作者：
Kim S;Thiessen PA;Bolton EE;Chen J;Fu G;Gindulyte A;Han L;He J;He S;Shoemaker BA;Wang J;Yu B;Zhang J;Bryant SH
通讯作者：
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影响因子：
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作者：
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作者：
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DOI：
10.1073/pnas.89.23.11322
发表时间：
1992-12-01
影响因子：
11.1
作者：
KING, RD;MUGGLETON, S;STERNBERG, MJE
通讯作者：
STERNBERG, MJE
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影响因子：
5.7
作者：
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