深度學習可助力新型抗生素發現
作者：
小柯機器人發布時間：2020/2/23 13:03:58
近日，美國麻省理工學院James J. Collins、Regina Barzilay等研究人員合作開發了一個可用於抗生素發現的深度學習方法。相關論文於2020年2月20日發表在《細胞》雜誌上。
研究人員表示，由於抗生素耐藥細菌的迅速出現，發現新抗生素的需求不斷增長。
為了應對這一挑戰，研究人員訓練了一個能夠預測抗菌活性分子的深層神經網絡。他們對多個化學文庫進行了預測，發現了來自「藥物再利用中心」的一種分子——halicin，該分子與常規抗生素在結構上有所不同，並顯示出對多種病原體（包括結核分枝桿菌和耐碳青黴烯的腸桿菌科）的殺菌活性。halicin還可以在鼠類模型中有效治療艱難梭菌和泛耐藥鮑曼不動桿菌感染。此外，從ZINC15資料庫收集的超過1.07億個分子的23個預測中，研究人員的模型鑑定了8種與已知抗生素在結構上相距較遠的抗菌化合物。這項工作突出了深度學習方法的實用性，其可通??過發現結構獨特的抗菌分子來擴展現有的抗生素庫。
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
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