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Jannis Born

Showing results (1-10 of 18) with videos related to

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Current Medicinal Chemistry|July 30, 2021
Trends in Deep Learning for Property-driven Drug DesignJannis Born, Matteo Manica
European Journal of Medicinal Chemistry|June 1, 2026
From fragments to drugs: How AI and in silico methods accelerate fragment-to-lead optimizationElnaz Aledavood, Jannis Born, Carmen Gil
Bioinformatics (Oxford, England)|July 12, 2021
TITAN: T-cell receptor specificity prediction with bimodal attention networksAnna Weber, Jannis Born, María Rodriguez Martínez
Plos One|June 1, 2017
Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual systemJannis Born, Juan M Galeazzi, Simon M Stringer
Nucleic Acids Research|May 14, 2020
PaccMann: a web service for interpretable anticancer compound sensitivity predictionJoris Cadow, Jannis Born, Matteo Manica, et al.
Journal of Chemical Information and Modeling|December 14, 2021
Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D ModelJannis Born, Tien Huynh, Astrid Stroobants, et al.
NPJ Precision Oncology|April 24, 2024
Overcoming limitations in current measures of drug response may enable AI-driven precision oncologyKatja Ovchinnikova, Jannis Born, Panagiotis Chouvardas, et al.
Cell Systems|April 23, 2026
Modeling chimeric antigen receptor response at the single-cell level with conditional optimal transportAlice Driessen, Jannis Born, Rocio Castellanos Rueda, et al.
Iscience|April 14, 2021
PaccMann<sup>RL</sup>: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learningJannis Born, Matteo Manica, Ali Oskooei, et al.
Journal of Chemical Information and Modeling|September 13, 2022
On the Choice of Active Site Sequences for Kinase-Ligand Affinity PredictionJannis Born, Yoel Shoshan, Tien Huynh, et al.
Pageof 2

Showing results (1-10 of 18) with videos related to

Sort By:
Pageof 2
Current Medicinal Chemistry|July 30, 2021
Trends in Deep Learning for Property-driven Drug DesignJannis Born, Matteo Manica
European Journal of Medicinal Chemistry|June 1, 2026
From fragments to drugs: How AI and in silico methods accelerate fragment-to-lead optimizationElnaz Aledavood, Jannis Born, Carmen Gil
Bioinformatics (Oxford, England)|July 12, 2021
TITAN: T-cell receptor specificity prediction with bimodal attention networksAnna Weber, Jannis Born, María Rodriguez Martínez
Plos One|June 1, 2017
Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual systemJannis Born, Juan M Galeazzi, Simon M Stringer
Nucleic Acids Research|May 14, 2020
PaccMann: a web service for interpretable anticancer compound sensitivity predictionJoris Cadow, Jannis Born, Matteo Manica, et al.
Journal of Chemical Information and Modeling|December 14, 2021
Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D ModelJannis Born, Tien Huynh, Astrid Stroobants, et al.
NPJ Precision Oncology|April 24, 2024
Overcoming limitations in current measures of drug response may enable AI-driven precision oncologyKatja Ovchinnikova, Jannis Born, Panagiotis Chouvardas, et al.
Cell Systems|April 23, 2026
Modeling chimeric antigen receptor response at the single-cell level with conditional optimal transportAlice Driessen, Jannis Born, Rocio Castellanos Rueda, et al.
Iscience|April 14, 2021
PaccMann<sup>RL</sup>: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learningJannis Born, Matteo Manica, Ali Oskooei, et al.
Journal of Chemical Information and Modeling|September 13, 2022
On the Choice of Active Site Sequences for Kinase-Ligand Affinity PredictionJannis Born, Yoel Shoshan, Tien Huynh, et al.
Pageof 2