Jannis Born
Orcid: 0000-0001-8307-5670
According to our database1,
Jannis Born
authored at least 27 papers
between 2018 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
2023
Regression Transformer enables concurrent sequence regression and generation for molecular language modelling.
Nat. Mac. Intell., April, 2023
Proceedings of the International Conference on Machine Learning, 2023
2022
Accelerating Molecular Discovery with Generative Language Models: A journey through the chemical space.
PhD thesis, 2022
J. Chem. Inf. Model., 2022
Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model.
J. Chem. Inf. Model., 2022
Regression Transformer: Concurrent Conditional Generation and Regression by Blending Numerical and Textual Tokens.
CoRR, 2022
A computational investigation of inventive spelling and the "Lesen durch Schreiben" method.
Comput. Educ. Artif. Intell., 2022
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022
2021
Patterns, 2021
Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2.
Mach. Learn. Sci. Technol., 2021
Bioinform., 2021
Lessons Learned from the Development and Application of Medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next.
Proceedings of the Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning, 2021
2020
PaccMann: a web service for interpretable anticancer compound sensitivity prediction.
Nucleic Acids Res., 2020
Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis.
CoRR, 2020
PaccMann<sup>RL</sup> on SARS-CoV-2: Designing antiviral candidates with conditional generative models.
CoRR, 2020
POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS).
CoRR, 2020
PaccMann<sup>RL</sup>: Designing Anticancer Drugs From Transcriptomic Data via Reinforcement Learning.
Proceedings of the Research in Computational Molecular Biology, 2020
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
2019
Reinforcement learning-driven de-novo design of anticancer compounds conditioned on biomolecular profiles.
CoRR, 2019
Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders.
CoRR, 2019
2018
PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks.
CoRR, 2018