Publications
Link to Google Scholar 🎓
2023
Chemical representation learning for toxicity prediction
Born, J., Markert, G., Janakarajan, N., Kimber, T. B., Volkamer, A., Martínez, M. R., & Manica, M.
Digital Discovery.
🔗: https://doi.org/10.1039/D2DD00099G
💻: https://github.com/PaccMann/chemical_representation_learning_for_toxicity_prediction
2022
Kinase similarity assessment pipeline for off-target prediction [v1.0]
Kimber, T. B., Sydow, D., & Volkamer, A.
Living Journal of Computational Molecular Science, 3,(1), 1599.
🔗: https://doi.org/10.33011/livecoms.3.1.1599
💻: https://github.com/volkamerlab/teachopencadd
TeachOpenCADD 2022: open source and FAIR Python pipelines to assist in structural bioinformatics and cheminformatics research
Sydow, D., Rodríguez-Guerra, J., Kimber, T. B., Schaller, D., Taylor, C. J., Chen, Y., Leja, M. Misra,
S., Wichmann, M., Ariamajd, A., & Volkamer, A.
Nucleic Acids Research, 50(W1), W753-W760.
🔗: https://doi.org/10.1093/nar/gkac267
💻: https://github.com/volkamerlab/teachopencadd
preprint: https://doi.org/10.26434/chemrxiv-2021-8x13n
2021
Maxsmi: maximizing molecular property prediction performance with confidence estimation using SMILES
augmentation and deep learning
Kimber, T. B., Gagnebin, M., & Volkamer, A.
Artificial Intelligence in the Life Sciences, 1, 100014.
🔗: https://doi.org/10.1016/j.ailsci.2021.100014
💻: https://github.com/volkamerlab/maxsmi
Deep Learning in Virtual Screening: Recent Applications and Developments
Kimber, T. B., Chen, Y., & Volkamer, A.
International Journal of Molecular Sciences, 22(9), 4435.
🔗: https://doi.org/10.3390/ijms22094435
💻: https://github.com/volkamerlab/DL_in_VS_review
2020
Revealing cytotoxic substructures in molecules using deep learning
Webel, H. E., Kimber, T. B., Radetzki, S., Neuenschwander, M., Nazaré, M., & Volkamer, A.
Journal of computer-aided molecular design, 34(7), 731-746.
🔗: https://doi.org/10.1007/s10822-020-00310-4
2019
Augmentation Is What You Need!
Tetko, I. V., Karpov, P., Bruno, E., Kimber, T. B., & Godin, G.
In International Conference on Artificial Neural Networks (pp. 831-835). Springer, Cham.
🔗: https://doi.org/10.1007/978-3-030-30493-5_79
2018
Synergy effect between convolutional neural networks and the multiplicity of SMILES for improvement of
molecular prediction
Kimber, T. B., Engelke, S., Tetko, I. V., Bruno, E., & Godin, G.
arXiv preprint, arXiv:1812.04439.
🔗: https://arxiv.org/abs/1812.04439