Cheminformatics articles within Nature

Featured

  • Article
    | Open Access

    A new discovery strategy, ‘reverse metabolomics’, facilitates high-throughput matching of mass spectrometry spectra in public untargeted metabolomics datasets, and a proof-of-concept experiment identified an association between microbial bile amidates and inflammatory bowel disease.

    • Emily C. Gentry
    • , Stephanie L. Collins
    •  & Pieter C. Dorrestein

  • Article |

    Computers equipped with a comprehensive knowledge-base of mechanistic steps augmented by physical-organic chemistry rules, as well as quantum mechanical and kinetic calculations, can use a reaction-network approach to analyse the mechanisms of cationic rearrangements.

    • Tomasz Klucznik
    • , Leonidas-Dimitrios Syntrivanis
    •  & Bartosz A. Grzybowski

  • Article |

    A forward-synthesis platform, Allchemy, computationally determines how to ‘close the circle’, or use waste chemicals to make valuable pharmaceutical or agrochemical products, ranking possible routes by environmental, geospatial, and other factors.

    • Agnieszka Wołos
    • , Dominik Koszelewski
    •  & Bartosz A. Grzybowski

  • Article |

    V-SYNTHES, a scalable and computationally cost-effective synthon-based approach to compound screening, identified compounds with a high affinity for CB2 and CB1 in a hierarchical structure-based screen of more than 11 billion compounds.

    • Arman A. Sadybekov
    • , Anastasiia V. Sadybekov
    •  & Vsevolod Katritch

  • Article |

    A synthetic route-planning algorithm, augmented with causal relationships that allow it to strategize over multiple steps, can design complex natural-product syntheses that are indistinguishable from those designed by human experts.

    • Barbara Mikulak-Klucznik
    • , Patrycja Gołębiowska
    •  & Bartosz A. Grzybowski

  • Article |

    All possible chemical transformations between amine and carboxylic acid groups are mapped using an automated string-based combinatorics method, showing that the properties and functions of the products vary considerably over all plausible reactions.

    • Babak Mahjour
    • , Yuning Shen
    •  & Tim Cernak

  • Letter |

    Human scientists make unrepresentative chemical reagent and reaction condition choices, and machine-learning algorithms trained on human-selected experiments are less capable of successfully predicting reaction outcomes than those trained on randomly generated experiments.

    • Xiwen Jia
    • , Allyson Lynch
    •  & Joshua Schrier

  • Perspective |

    This Perspective discusses the challenges associated with the prediction of chemical synthesis, in particular the reaction conditions required for organic transformations, and the role of machine-learning approaches in the prediction process.

    • Ian W. Davies

  • Article |

    Deep neural networks and Monte Carlo tree search can plan chemical syntheses by training models on a huge database of published reactions; their predicted synthetic routes cannot be distinguished from those a human chemist would design.

    • Marwin H. S. Segler
    • , Mike Preuss
    •  & Mark P. Waller

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