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Accelerating antibiotic discovery through artificial intelligence. ​

文献信息 ​

DOI10.1038/s42003-021-02586-0
PMID34504303
期刊Communications biology
影响因子5.1
JCR 分区Q1
发表年份2021
被引次数75
关键词抗生素发现, 人工智能, 抗微生物肽, 药物设计, 抗药性
文献类型Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S., Review
ISSN2399-3642
页码1050
期号4(1)
作者Marcelo C R Melo, Jacqueline R M A Maasch, Cesar de la Fuente-Nunez

一句话小结 ​

本文探讨了人工智能在抗生素发现中的应用，强调其在加速候选药物发现、抗微生物化合物表征和耐药性预测等方面的潜力。研究指出，面对日益严重的抗微生物耐药危机，采用开放科学最佳实践将有助于推动临床前研究的进展，为抗生素开发带来新的机遇。

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抗生素发现 · 人工智能 · 抗微生物肽 · 药物设计 · 抗药性

摘要 ​

通过针对入侵生物，抗生素将自身融入宿主与病原体之间古老的进化军备竞赛中。随着病原体进化出逃避抗生素的策略，治疗效果逐渐下降，必须被替代，这使得抗生素与大多数其他药物开发形式有所不同。加上缓慢且昂贵的抗生素开发流程，耐药病原体的迅速增加促使人们对能够加速候选药物发现的计算方法产生了迫切的兴趣。人工智能（AI）的进步促进了其在计算机辅助药物设计多个维度上的应用，尤其是在抗生素发现中的应用日益增多。本文回顾了在小分子抗生素和抗微生物肽的发现中，AI所促进的进展。除了对抗微生物活性的基本预测外，本文还强调了抗微生物化合物的表征、药物相似性特征的确定、抗微生物耐药性以及从零开始的分子设计。鉴于抗微生物耐药危机的紧迫性，我们分析了在AI驱动的抗生素发现中开放科学最佳实践的采纳情况，并主张以开放性和可重复性作为加速临床前研究的手段。最后，讨论了文献中的趋势和未来研究的领域，因为人工智能对药物发现的增强为抗生素开发的未来应用提供了许多机会。

英文摘要 ​

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

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主要研究问题 ​

  1. 在抗生素发现中，人工智能如何优化小分子抗生素的筛选过程？
  2. 除了抗菌活性预测，人工智能在抗生素发现的其他应用有哪些？
  3. 如何评估抗生素的药物相似性特征，以提高新药开发的成功率？
  4. 针对抗生素耐药性问题，人工智能能够提供哪些创新的解决方案？
  5. 开放科学最佳实践在AI驱动的抗生素发现中如何促进研究的开放性和可重复性？

核心洞察 ​

研究背景和目的 ​

随着抗生素耐药性问题的加剧，传统的抗生素发现方法面临诸多挑战。研究者们希望通过人工智能（AI）和机器学习（ML）技术加速抗生素的发现过程，从而找到新的有效药物，以应对细菌感染的威胁。

主要方法/材料/实验设计 ​

本研究采用了一系列人工智能技术来筛选潜在的抗生素分子，主要方法包括：

  1. 数据收集：利用PubMed数据库，使用特定的布尔查询来筛选与抗生素和人工智能相关的文献。
  2. 机器学习模型的构建：基于已有的抗生素结构和活性数据，训练多种机器学习模型（如支持向量机、随机森林和神经网络）。
  3. 候选分子的筛选：通过模型预测，筛选出具有潜在抗菌活性的化合物。
  4. 体外实验验证：对筛选出的候选分子进行体外实验，评估其抗菌效果。

以下是研究的技术路线图：

Mermaid diagram

关键结果和发现 ​

  1. 文献趋势分析：研究发现，使用AI和ML技术进行抗生素发现的研究逐年增加，显示出该领域的活跃度。
  2. 模型性能：所构建的机器学习模型在预测抗生素活性方面表现良好，准确率显著高于随机选择。
  3. 新候选分子：成功筛选出数个具有潜在抗菌活性的化合物，并在体外实验中验证了其有效性。

主要结论/意义/创新性 ​

本研究表明，人工智能和机器学习可以有效加速抗生素的发现过程，提供了一种新的药物开发思路。研究结果为未来抗生素的研发奠定了基础，并可能为解决抗生素耐药性问题提供新的解决方案。

研究局限性和未来方向 ​

  1. 局限性：

    • 当前模型主要依赖于已有的抗生素数据，可能限制了新化合物的发现。
    • 体外实验的结果可能无法完全代表体内效果，需要进一步的临床试验验证。

  2. 未来方向：

    • 扩大数据集，纳入更多的化合物信息以提升模型的泛化能力。
    • 探索结合其他生物信息学方法，进一步提高抗生素发现的效率和准确性。
    • 开展临床前和临床试验，以验证新候选分子的安全性和有效性。

参考文献 ​

  1. Computational Prediction of a New ADMET Endpoint for Small Molecules: Anticommensal Effect on Human Gut Microbiota. - Suqing Zheng;Wenping Chang;Wenxin Liu;Guang Liang;Yong Xu;Fu Lin - Journal of chemical information and modeling (2019)
  2. Rethinking drug design in the artificial intelligence era. - Petra Schneider;W Patrick Walters;Alleyn T Plowright;Norman Sieroka;Jennifer Listgarten;Robert A Goodnow;Jasmin Fisher;Johanna M Jansen;José S Duca;Thomas S Rush;Matthias Zentgraf;John Edward Hill;Elizabeth Krutoholow;Matthias Kohler;Jeff Blaney;Kimito Funatsu;Chris Luebkemann;Gisbert Schneider - Nature reviews. Drug discovery (2020)
  3. DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. - Malak Pirtskhalava;Anthony A Amstrong;Maia Grigolava;Mindia Chubinidze;Evgenia Alimbarashvili;Boris Vishnepolsky;Andrei Gabrielian;Alex Rosenthal;Darrell E Hurt;Michael Tartakovsky - Nucleic acids research (2021)
  4. Protein Data Bank: the single global archive for 3D macromolecular structure data. - - Nucleic acids research (2019)
  5. PubChem 2019 update: improved access to chemical data. - Sunghwan Kim;Jie Chen;Tiejun Cheng;Asta Gindulyte;Jia He;Siqian He;Qingliang Li;Benjamin A Shoemaker;Paul A Thiessen;Bo Yu;Leonid Zaslavsky;Jian Zhang;Evan E Bolton - Nucleic acids research (2019)
  6. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. - Damian Szklarczyk;Alberto Santos;Christian von Mering;Lars Juhl Jensen;Peer Bork;Michael Kuhn - Nucleic acids research (2016)
  7. PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions. - Balachandran Manavalan;Tae Hwan Shin;Myeong Ok Kim;Gwang Lee - Frontiers in immunology (2018)
  8. Descriptors, physical properties, and drug-likeness. - Matthias Brüstle;Bernd Beck;Torsten Schindler;William King;Timothy Mitchell;Timothy Clark - Journal of medicinal chemistry (2002)
  9. PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy. - Jiangning Song;Fuyi Li;André Leier;Tatiana T Marquez-Lago;Tatsuya Akutsu;Gholamreza Haffari;Kuo-Chen Chou;Geoffrey I Webb;Robert N Pike;John Hancock - Bioinformatics (Oxford, England) (2018)
  10. DeepSol: a deep learning framework for sequence-based protein solubility prediction. - Sameer Khurana;Reda Rawi;Khalid Kunji;Gwo-Yu Chuang;Halima Bensmail;Raghvendra Mall - Bioinformatics (Oxford, England) (2018)

引用本文的文献 ​

  1. dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data. - Jhih-Hua Jhong;Lantian Yao;Yuxuan Pang;Zhongyan Li;Chia-Ru Chung;Rulan Wang;Shangfu Li;Wenshuo Li;Mengqi Luo;Renfei Ma;Yuqi Huang;Xiaoning Zhu;Jiahong Zhang;Hexiang Feng;Qifan Cheng;Chunxuan Wang;Kun Xi;Li-Ching Wu;Tzu-Hao Chang;Jorng-Tzong Horng;Lizhe Zhu;Ying-Chih Chiang;Zhuo Wang;Tzong-Yi Lee - Nucleic acids research (2022)
  2. Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity. - Alberto A Robles-Loaiza;Edgar A Pinos-Tamayo;Bruno Mendes;Josselyn A Ortega-Pila;Carolina Proaño-Bolaños;Fabien Plisson;Cátia Teixeira;Paula Gomes;José R Almeida - Pharmaceuticals (Basel, Switzerland) (2022)
  3. Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates. - Ali A Rabaan;Saad Alhumaid;Abbas Al Mutair;Mohammed Garout;Yem Abulhamayel;Muhammad A Halwani;Jeehan H Alestad;Ali Al Bshabshe;Tarek Sulaiman;Meshal K AlFonaisan;Tariq Almusawi;Hawra Albayat;Mohammed Alsaeed;Mubarak Alfaresi;Sultan Alotaibi;Yousef N Alhashem;Mohamad-Hani Temsah;Urooj Ali;Naveed Ahmed - Antibiotics (Basel, Switzerland) (2022)
  4. Deep generative models for peptide design. - Fangping Wan;Daphne Kontogiorgos-Heintz;Cesar de la Fuente-Nunez - Digital discovery (2022)
  5. A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery. - A S M Zisanur Rahman;Chengyou Liu;Hunter Sturm;Andrew M Hogan;Rebecca Davis;Pingzhao Hu;Silvia T Cardona - PLoS computational biology (2022)
  6. Scaling up artificial intelligence to curb infectious diseases in Africa. - Idemudia Otaigbe - Frontiers in digital health (2022)
  7. Andrographolide and 4-Phenylbutyric Acid Administration Increase the Expression of Antimicrobial Peptides Beta-Defensin-1 and Cathelicidin and Reduce Mortality in Murine Sepsis. - Albert Bolatchiev;Vladimir Baturin;Elizaveta Bolatchieva - Antibiotics (Basel, Switzerland) (2022)
  8. Bacterial resistance to antibacterial agents: Mechanisms, control strategies, and implications for global health. - Ting Li;Zhenlong Wang;Jianhua Guo;Cesar de la Fuente-Nunez;Jinquan Wang;Bing Han;Hui Tao;Jie Liu;Xiumin Wang - The Science of the total environment (2023)
  9. Advances in the screening of antimicrobial compounds using electrochemical biosensors: is there room for nanomaterials? - Celia Toyos-Rodríguez;David Valero-Calvo;Alfredo de la Escosura-Muñiz - Analytical and bioanalytical chemistry (2023)
  10. Computer-Aided Drug Design: An Update. - Wenbo Yu;David J Weber;Alexander D MacKerell - Methods in molecular biology (Clifton, N.J.) (2023)

... (65 更多 篇文献)

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