Filip Miljkovic

According to our database, Filip Miljkovic authored at least 13 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation.

Ya Chen, Thomas Seidel, Roxane Axel Jacob, Steffen Hirte, Angelica Mazzolari, Alessandro Pedretti, Giulio Vistoli, Thierry Langer, Filip Miljkovic, Johannes Kirchmair

J. Chem. Inf. Model., 2024

2023

Protein kinase inhibitors.

Elena Xerxa, Filip Miljkovic, Jürgen Bajorath

Dataset, May, 2023

Interpretable bilinear attention network with domain adaptation improves drug-target prediction.

Peizhen Bai, Filip Miljkovic, Bino John, Haiping Lu

Nat. Mac. Intell., February, 2023

2022

Candidate compounds from the design of covalent Bruton's tyrosine kinase (BTK) inhibitors via focused deep generative modeling.

Atsushi Yoshimori, Filip Miljkovic, Jürgen Bajorath

Dataset, January, 2022

Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images.

Andrés Martínez Mora, Vigneshwari Subramanian, Filip Miljkovic

J. Comput. Aided Mol. Des., 2022

2021

Hierarchical Clustering Split for Low-Bias Evaluation of Drug-Target Interaction Prediction.

Peizhen Bai, Filip Miljkovic, Yan Ge, Nigel Greene, Bino John, Haiping Lu

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021

2020

Assessing the information content of structural and protein-ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning.

Raquel Rodríguez-Pérez, Filip Miljkovic, Jürgen Bajorath

J. Cheminformatics, 2020

Data structures for computational compound promiscuity analysis and exemplary applications to inhibitors of the human kinome.

Filip Miljkovic, Jürgen Bajorath

J. Comput. Aided Mol. Des., 2020

2019

Promiscuous compounds with activity against different target classes.

Christian Feldmann, Filip Miljkovic, Dimitar G. Yonchev, Jürgen Bajorath

Dataset, November, 2019

Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes.

Filip Miljkovic, Raquel Rodríguez-Pérez, Jürgen Bajorath

Dataset, August, 2019

Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes.

Filip Miljkovic, Raquel Rodríguez-Pérez, Jürgen Bajorath

Dataset, August, 2019

Promiscuity cliffs (PCs), promiscuity cliff pathways (PCPs), and promiscuity hubs (PHs) formed by inhibitors of human kinases.

Filip Miljkovic, Jürgen Bajorath

Dataset, March, 2019

Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome.

Filip Miljkovic, Martin Vogt, Jürgen Bajorath

J. Comput. Aided Mol. Des., 2019