AI in Drug Discovery

Artificial Intelligence (AI) methods have been shown to be able to design new molecules and to accurately foresee their role in the human body. New drugs will be safer and more effective than they have ever been. Researchers of JKU have won the Tox21 Data Challenge demonstrating that neural AIs can find toxic effects. Günter Klambauer leads an award-winning team of scientists developing AIs that design, improve and assess new drugs. Several AI-generated drugs are currently tested for their ability to inhibit SARS-CoV-2.

recent publications in AI in Drug Discovery:

  1. AI4Science
    Robust task-specific adaption of models for drug-target interaction prediction
    Svensson, E., Hoedt, P., Hochreiter, S., and Klambauer, G.
    In NeurIPS 2022 AI for Science: Progress and Promises 2022
  2. ML4Molecules
    Task-conditioned modeling of drug-target interactions
    Svensson, E., Hoedt, P., Hochreiter, S., and Klambauer, G.
    In ELLIS Machine Learning for Molecule Discovery Workshop 2022
  3. JCIM
    Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks
    Seidl, P., Renz, P., Dyubankova, N., Neves, P., Verhoeven, J., Wegner, J., Segler, M., Hochreiter, S., and Klambauer, G.
    Journal of Chemical Information and Modeling 2022
  4. QSAR
    Benchmarking recent Deep Learning methods on the extended Tox21 data set
    Seidl, P., Halmich, C., Mayr, A., Vall, A., Ruch, P., Hochreiter, S., and Klambauer, G.
    In 2021
  5. Frontiers in AI
    The Promise of AI for DILI Prediction
    Vall, A., Sabnis, Y., Shi, J., Class, R., Hochreiter, S., and Klambauer, G.
    Frontiers in Artificial Intelligence 2021
  6. arXiv
    Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction
    arXiv preprint arXiv:2104.03279 2021
  7. QSAR2021
    Comparative assessment of interpretability methods of deep activity models for hERG
    Schimunek, J., Friedrich, L., Kuhn, D., Hochreiter, S., Rippmann, F., and Klambauer, G.
  8. Springer
    Industry-Scale Application and Evaluation of Deep Learning for Drug Target Prediction
    Sturm, N., Mayr, A., Le Van, T., Chupakhin, V., Ceulemans, H., Wegner, J., Golib-Dzib, J., Jeliazkova, N., Vandriessche, Y., Böhm, S., Cima, V., Martinovic, J., Greene, N., Vander Aa, T., Ashby, T., Hochreiter, S., Engkvist, O., Klambauer, G., and Chen, H.
  9. On Failure Modes in Molecule Generation and Optimization
    Renz, P., Van Rompaey, D., Wegner, J., Hochreiter, S., and Klambauer, G.
  10. Community Assessment to Advance Computational Prediction of Cancer Drug Combinations in a Pharmacogenomic Screen
    Menden, M., Wang, D., Mason, M., Szalai, B., Bulusu, K., Guan, Y., Yu, T., Kang, J., Jeon, M., Wolfinger, R., Nguyen, T., Zaslavskiy, M., Jang, I., Ghazoui, Z., Ahsen, M., Vogel, R., Neto, E., Norman, T., Tang, E., Garnett, M., Veroli, G., Fawell, S., Stolovitzky, G., Guinney, J., Dry, J., and Saez-Rodriguez, J.
  11. Machine Learning in Drug Discovery
    Klambauer, G., Hochreiter, S., and Rarey, M.
  12. Interpretable Deep Learning in Drug Discovery
    Preuer, K., Klambauer, G., Rippmann, F., Hochreiter, S., and Unterthiner, T.
  13. NeurIPS
    Uncertainty Estimation Methods to Support Decision-Making in Early Phases of Drug Discovery
    Renz, P., Hochreiter, S., and Klambauer, G.
  14. Machine Learning in Drug Discovery
    Hochreiter, S., Klambauer, G., and Rarey, M.
  15. Large-Scale Comparison of Machine Learning Methods for Drug Target Prediction on ChEMBL
    Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J., Ceulemans, H., Clevert, D., and Hochreiter, S.
  16. DeepSynergy: Predicting Anti-Cancer Drug Synergy with Deep Learning
    Preuer, K., Lewis, R., Hochreiter, S., Bender, A., Bulusu, K., and Klambauer, G.
  17. Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery
    Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S., and Klambauer, G.
  18. Rectified Factor Networks for Biclustering of Omics Data
    Clevert, D., Unterthiner, T., Povysil, G., and Hochreiter, S.
  19. Panelcn.MOPS: Copy-Number Detection in Targeted NGS Panel Data for Clinical Diagnostics
    Povysil, G., Tzika, A., Vogt, J., Haunschmid, V., Messiaen, L., Zschocke, J., Klambauer, G., Hochreiter, S., and Wimmer, K.