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@ARTICLE{Periwal:910709,
      author       = {Periwal, Vinita and Bassler, Stefan and Andrejev, Sergej
                      and Gabrielli, Natalia and Patil, Kaustubh and Typas,
                      Athanasios and Patil, Kiran Raosaheb},
      title        = {{B}ioactivity assessment of natural compounds using machine
                      learning models trained on target similarity between drugs},
      journal      = {PLoS Computational Biology},
      volume       = {18},
      number       = {4},
      issn         = {1553-734X},
      address      = {San Francisco, Calif.},
      publisher    = {Public Library of Science},
      reportid     = {FZJ-2022-04080},
      pages        = {e1010029 -},
      year         = {2022},
      abstract     = {Natural compounds constitute a rich resource of potential
                      small molecule therapeutics. While experimental access to
                      this resource is limited due to its vast diversity and
                      difficulties in systematic purification, computational
                      assessment of structural similarity with known therapeutic
                      molecules offers a scalable approach. Here, we assessed
                      functional similarity between natural compounds and approved
                      drugs by combining multiple chemical similarity metrics and
                      physicochemical properties using a machine-learning
                      approach. We computed pairwise similarities between 1410
                      drugs for training classification models and used the drugs
                      shared protein targets as class labels. The best performing
                      models were random forest which gave an average area under
                      the ROC of 0.9, Matthews correlation coefficient of 0.35,
                      and F1 score of 0.33, suggesting that it captured the
                      structure-activity relation well. The models were then used
                      to predict protein targets of circa 11k natural compounds by
                      comparing them with the drugs. This revealed therapeutic
                      potential of several natural compounds, including those with
                      support from previously published sources as well as those
                      hitherto unexplored. We experimentally validated one of the
                      predicted pair’s activities, viz., Cox-1 inhibition by
                      5-methoxysalicylic acid, a molecule commonly found in tea,
                      herbs and spices. In contrast, another natural compound,
                      4-isopropylbenzoic acid, with the highest similarity score
                      when considering most weighted similarity metric but not
                      picked by our models, did not inhibit Cox-1. Our results
                      demonstrate the utility of a machine-learning approach
                      combining multiple chemical features for uncovering protein
                      binding potential of natural compounds.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35468126},
      UT           = {WOS:000792604600002},
      doi          = {10.1371/journal.pcbi.1010029},
      url          = {https://juser.fz-juelich.de/record/910709},
}