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@ARTICLE{Oestreich:277318,
      author       = {Oestreich, Marie and Merdivan, Erinc and Lee, Michael and
                      Schultze, Joachim L and Piraud, Marie and Becker, Matthias},
      title        = {{D}rug{D}iff: small molecule diffusion model with flexible
                      guidance towards molecular properties.},
      journal      = {Journal of cheminformatics},
      volume       = {17},
      number       = {1},
      issn         = {1758-2946},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DZNE-2025-00381},
      pages        = {23},
      year         = {2025},
      abstract     = {With the cost/yield-ratio of drug development becoming
                      increasingly unfavourable, recent work has explored machine
                      learning to accelerate early stages of the development
                      process. Given the current success of deep generative models
                      across domains, we here investigated their application to
                      the property-based proposal of new small molecules for drug
                      development. Specifically, we trained a latent diffusion
                      model-DrugDiff-paired with predictor guidance to generate
                      novel compounds with a variety of desired molecular
                      properties. The architecture was designed to be highly
                      flexible and easily adaptable to future scenarios. Our
                      experiments showed successful generation of unique, diverse
                      and novel small molecules with targeted properties. The code
                      is available at https://github.com/MarieOestreich/DrugDiff .
                      SCIENTIFIC CONTRIBUTION: This work expands the use of
                      generative modelling in the field of drug development from
                      previously introduced models for proteins and RNA to the
                      here presented application to small molecules. With small
                      molecules making up the majority of drugs, but
                      simultaneously being difficult to model due to their
                      elaborate chemical rules, this work tackles a new level of
                      difficulty in comparison to sequence-based molecule
                      generation as is the case for proteins and RNA.
                      Additionally, the demonstrated framework is highly flexible,
                      allowing easy addition or removal of considered molecular
                      properties without the need to retrain the model, making it
                      highly adaptable to diverse research settings and it shows
                      compelling performance for a wide variety of targeted
                      molecular properties.},
      keywords     = {Drug development (Other) / Generative modelling (Other) /
                      Latent diffusion (Other) / Targeted generation (Other)},
      cin          = {AG Becker / AG Schultze / PRECISE},
      ddc          = {540},
      cid          = {I:(DE-2719)5000079 / I:(DE-2719)1013038 /
                      I:(DE-2719)1013031},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354) / 352
                      - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-354 / G:(DE-HGF)POF4-352},
      experiment   = {EXP:(DE-2719)PRECISE-20190321},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40001177},
      doi          = {10.1186/s13321-025-00965-x},
      url          = {https://pub.dzne.de/record/277318},
}