Filip Miljković

1.2k total citations · 1 hit paper
43 papers, 806 citations indexed

About

Filip Miljković is a scholar working on Computational Theory and Mathematics, Molecular Biology and Pharmacology. According to data from OpenAlex, Filip Miljković has authored 43 papers receiving a total of 806 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Computational Theory and Mathematics, 30 papers in Molecular Biology and 11 papers in Pharmacology. Recurrent topics in Filip Miljković's work include Computational Drug Discovery Methods (36 papers), Microbial Natural Products and Biosynthesis (11 papers) and Protein Structure and Dynamics (9 papers). Filip Miljković is often cited by papers focused on Computational Drug Discovery Methods (36 papers), Microbial Natural Products and Biosynthesis (11 papers) and Protein Structure and Dynamics (9 papers). Filip Miljković collaborates with scholars based in Germany, Sweden and United States. Filip Miljković's co-authors include Jürgen Bajorath, Bino John, Haiping Lu, Raquel Rodríguez-Pérez, Huabin Hu, Beth Williamson, Nigel Greene, Aleksandar M. Veselinović, Alla P. Toropova and Jovana B. Veselinović and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Medicinal Chemistry and Molecules.

In The Last Decade

Filip Miljković

41 papers receiving 788 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Interpretable bilinear attention network with domain adaptation improves drug–target prediction

2023 170 citations Filip Miljković, Bino John et al. Nature Machine Intelligence profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Filip Miljković Germany	16	554	460	163	97	91	43	806
Eloy Félix United Kingdom	6	625 1.1×	545 1.2×	230 1.4×	68 0.7×	88 1.0×	11	1.0k
Guixia Liu China	17	765 1.4×	773 1.7×	121 0.7×	73 0.8×	93 1.0×	22	1.2k
John W. Mayfield United States	3	581 1.0×	542 1.2×	207 1.3×	89 0.9×	102 1.1×	3	936
Vishal B. Siramshetty United States	16	488 0.9×	443 1.0×	88 0.5×	49 0.5×	67 0.7×	24	848
Mélaine A. Kuenemann France	14	518 0.9×	544 1.2×	270 1.7×	106 1.1×	91 1.0×	23	994
Barbara Zdrazil Austria	16	468 0.8×	616 1.3×	102 0.6×	107 1.1×	95 1.0×	47	1.1k
Tianbiao Yang China	12	403 0.7×	473 1.0×	148 0.9×	60 0.6×	55 0.6×	17	664
Lewis Mervin United Kingdom	15	340 0.6×	410 0.9×	149 0.9×	102 1.1×	48 0.5×	32	736
Jocelyn Sunseri United States	7	629 1.1×	637 1.4×	240 1.5×	130 1.3×	87 1.0×	8	996
David A. Cosgrove United Kingdom	13	457 0.8×	396 0.9×	137 0.8×	118 1.2×	78 0.9×	22	654

Countries citing papers authored by Filip Miljković

Since Specialization

Citations

This map shows the geographic impact of Filip Miljković's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Filip Miljković with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Filip Miljković more than expected).

Fields of papers citing papers by Filip Miljković

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Filip Miljković. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Filip Miljković. The network helps show where Filip Miljković may publish in the future.

Co-authorship network of co-authors of Filip Miljković

This figure shows the co-authorship network connecting the top 25 collaborators of Filip Miljković. A scholar is included among the top collaborators of Filip Miljković based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Filip Miljković. Filip Miljković is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Miljković, Filip, et al.. (2025). Mask-prior-guided denoising diffusion improves inverse protein folding. Nature Machine Intelligence. 7(6). 876–888. 1 indexed citations

Karlsson, Isabella, Anja Ekdahl, Cecilia Ericsson, et al.. (2025). Metabolite Identification Data in Drug Discovery, Part 1: Data Generation and Trend Analysis. Molecular Pharmaceutics. 22(11). 6788–6802. 1 indexed citations

Chen, Ya, Susanne Winiwarter, R. Jacob, et al.. (2025). Metabolite Identification Data in Drug Discovery, Part 2: Site-of-Metabolism Annotation, Analysis, and Exploration for Machine Learning. Molecular Pharmaceutics. 22(11). 6772–6787. 1 indexed citations

Miljković, Filip & Jürgen Bajorath. (2024). Kinase Drug Discovery: Impact of Open Science and Artificial Intelligence. Molecular Pharmaceutics. 21(10). 4849–4859. 2 indexed citations

Miljković, Filip, Scott Davies, Karolina Kwapień, et al.. (2024). Closing the Design–Make–Test–Analyze Loop: Interplay between Experiments and Predictions Drives PROTACs Bioavailability. Journal of Medicinal Chemistry. 67(22). 20242–20257. 6 indexed citations

Chen, Ya, Thomas Seidel, Angelica Mazzolari, et al.. (2024). Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation. Journal of Chemical Information and Modeling. 64(2). 348–358. 5 indexed citations

Miljković, Filip & José L. Medina‐Franco. (2024). Artificial intelligence-open science symbiosis in chemoinformatics. SHILAP Revista de lepidopterología. 5. 100096–100096. 10 indexed citations

Miljković, Filip, et al.. (2023). Interpretable bilinear attention network with domain adaptation improves drug–target prediction. Nature Machine Intelligence. 5(2). 126–136. 170 indexed citations breakdown →

Miljković, Filip, et al.. (2023). Data-Driven Global Assessment of Protein Kinase Inhibitors with Emphasis on Covalent Compounds. Journal of Medicinal Chemistry. 66(11). 7657–7665. 10 indexed citations

10.

Subramanian, Vigneshwari, et al.. (2022). Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images. Journal of Computer-Aided Molecular Design. 36(6). 443–457. 7 indexed citations

11.

Obrezanova, Olga, Thomas M. Whitehead, Andreas Bender, et al.. (2022). Prediction of In Vivo Pharmacokinetic Parameters and Time–Exposure Curves in Rats Using Machine Learning from the Chemical Structure. Molecular Pharmaceutics. 19(5). 1488–1504. 46 indexed citations

12.

Miljković, Filip, et al.. (2022). Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug–drug interaction prediction. CPT Pharmacometrics & Systems Pharmacology. 11(12). 1560–1568. 22 indexed citations

13.

Yoshimori, Atsushi, Filip Miljković, & Jürgen Bajorath. (2022). Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling. Molecules. 27(2). 570–570. 9 indexed citations

14.

Miljković, Filip, et al.. (2022). Evaluating the performance of machine‐learning regression models for pharmacokinetic drug–drug interactions. CPT Pharmacometrics & Systems Pharmacology. 12(1). 122–134. 12 indexed citations

15.

Trapotsi, Maria‐Anna, Elizabeth Mouchet, Guy Williams, et al.. (2022). Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature. ACS Chemical Biology. 17(7). 1733–1744. 25 indexed citations

16.

Mogemark, Mickael, et al.. (2022). Interpretation of multi-task clearance models from molecular images supported by experimental design. SHILAP Revista de lepidopterología. 2. 100048–100048. 2 indexed citations

17.

Miljković, Filip, Olga Obrezanova, Beth Williamson, et al.. (2021). Machine Learning Models for Human In Vivo Pharmacokinetic Parameters with In-House Validation. Molecular Pharmaceutics. 18(12). 4520–4530. 59 indexed citations

18.

Miljković, Filip, Raquel Rodríguez-Pérez, & Jürgen Bajorath. (2021). Impact of Artificial Intelligence on Compound Discovery, Design, and Synthesis. ACS Omega. 6(49). 33293–33299. 30 indexed citations

19.

Feldmann, Christian, et al.. (2019). Identifying Promiscuous Compounds with Activity against Different Target Classes. Molecules. 24(22). 4185–4185. 15 indexed citations

20.

Miljković, Filip & Jürgen Bajorath. (2018). Data-Driven Exploration of Selectivity and Off-Target Activities of Designated Chemical Probes. Molecules. 23(10). 2434–2434. 9 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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