% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Keyl:304476,
      author       = {P. Keyl and J. Keyl and A. Mock$^*$ and G. Dernbach and L.
                      H. Mochmann and N. Kiermeyer$^*$ and P. Jurmeister$^*$ and
                      M. Bockmayr and R. F. Schwarz and G. Montavon and K.-R.
                      Müller and F. Klauschen$^*$},
      title        = {{N}eural interaction explainable {AI} predicts drug
                      response across cancers.},
      journal      = {NAR: cancer},
      volume       = {7},
      number       = {3},
      issn         = {2632-8674},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2025-01868},
      pages        = {zcaf029},
      year         = {2025},
      abstract     = {Personalized treatment selection is crucial for cancer
                      patients due to the high variability in drug response. While
                      actionable mutations can increasingly inform treatment
                      decisions, most therapies still rely on population-based
                      approaches. Here, we introduce neural interaction
                      explainable AI (NeurixAI), an explainable and highly
                      scalable deep learning framework that models drug-gene
                      interactions and identifies transcriptomic patterns linked
                      with drug response. Trained on data from 546 646 drug
                      perturbation experiments involving 1135 drugs and molecular
                      profiles from 476 tumors, NeurixAI accurately predicted
                      treatment responses for 272 targeted and 30 chemotherapeutic
                      drugs in unseen tumor samples (Spearman's rho >0.2),
                      maintaining high performance on an external validation set.
                      Additionally, NeurixAI identified the anticancer potential
                      of 160 repurposed non-cancer drugs. Using explainable
                      artificial intelligence (xAI), our framework uncovered key
                      genes influencing drug response at the individual tumor
                      level and revealed both known and novel mechanisms of drug
                      resistance. These findings demonstrate the potential of
                      integrating transcriptomics with xAI to optimize cancer
                      treatment, enable drug repurposing, and identify new
                      therapeutic targets.},
      keywords     = {Humans / Neoplasms: drug therapy / Neoplasms: genetics /
                      Antineoplastic Agents: therapeutic use / Antineoplastic
                      Agents: pharmacology / Artificial Intelligence / Drug
                      Repositioning / Transcriptome / Precision Medicine: methods
                      / Gene Expression Profiling / Drug Resistance, Neoplasm:
                      genetics / Deep Learning / Antineoplastic Agents (NLM
                      Chemicals)},
      cin          = {MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40918644},
      pmc          = {pmc:PMC12409417},
      doi          = {10.1093/narcan/zcaf029},
      url          = {https://inrepo02.dkfz.de/record/304476},
}