近日，美国麻省理工学院James J. Collins、Regina Barzilay等研究人员合作开发了一个可用于抗生素发现的深度学习方法。相关论文于2020年2月20日发表在《细胞》杂志上。
研究人员表示，由于抗生素耐药细菌的迅速出现，发现新抗生素的需求不断增长。
为了应对这一挑战，研究人员训练了一个能够预测抗菌活性分子的深层神经网络。他们对多个化学文库进行了预测，发现了来自“药物再利用中心”的一种分子——halicin，该分子与常规抗生素在结构上有所不同，并显示出对多种病原体（包括结核分枝杆菌和耐碳青霉烯的肠杆菌科）的杀菌活性。halicin还可以在鼠类模型中有效治疗艰难梭菌和泛耐药鲍曼不动杆菌感染。此外，从ZINC15数据库收集的超过1.07亿个分子的23个预测中，研究人员的模型鉴定了8种与已知抗生素在结构上相距较远的抗菌化合物。这项工作突出了深度学习方法的实用性，其可通??过发现结构独特的抗菌分子来扩展现有的抗生素库。
附：英文原文
Title: A Deep Learning Approach to Antibiotic Discovery
Author: Jonathan M. Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M. Donghia, Craig R. MacNair, Shawn French, Lindsey A. Carfrae, Zohar Bloom-Ackerman, Victoria M. Tran, Anush Chiappino-Pepe, Ahmed H. Badran, Ian W. Andrews, Emma J. Chory, George M. Church, Eric D. Brown, Tommi S. Jaakkola, Regina Barzilay, James J. Collins
Issue&Volume: 2020/02/20
Abstract: Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub—halicin—that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.
DOI: 10.1016/j.cell.2020.01.021
Source: https://www.cell.com/cell/fulltext/S0092-8674(20)30102-1