Miha Škalič

2.4k total citations · 2 hit papers
11 papers, 1.4k citations indexed

About

Miha Škalič is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Miha Škalič has authored 11 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computational Theory and Mathematics, 7 papers in Molecular Biology and 7 papers in Materials Chemistry. Recurrent topics in Miha Škalič's work include Computational Drug Discovery Methods (9 papers), Machine Learning in Materials Science (7 papers) and Protein Structure and Dynamics (4 papers). Miha Škalič is often cited by papers focused on Computational Drug Discovery Methods (9 papers), Machine Learning in Materials Science (7 papers) and Protein Structure and Dynamics (4 papers). Miha Škalič collaborates with scholars based in Spain, Germany and Switzerland. Miha Škalič's co-authors include José Jiménez-Luna, Gianni De Fabritiis, Gerard Martínez-Rosell, Davide Sabbadin, Juan Carlos Entizne, Eduardo Eyras, Juan L. Trincado, Gerald Hysenaj, Babita Singh and David J. Elliott and has published in prestigious journals such as Bioinformatics, Genome biology and Journal of Chemical Information and Modeling.

In The Last Decade

Miha Škalič

11 papers receiving 1.4k citations

Hit Papers

align trajectories sort by overperformance

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.

K_DEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

2018 637 citations José Jiménez-Luna, Miha Škalič et al. Journal of Chemical Information and Modeling profile →
SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions

2018 355 citations Juan L. Trincado, Juan Carlos Entizne et al. Genome biology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Miha Škalič Spain	9	1.1k	844	445	104	98	11	1.4k
Paweł Siedlecki Poland	17	1.6k 1.5×	910 1.1×	340 0.8×	62 0.6×	144 1.5×	36	2.0k
Michael Hsing Canada	19	950 0.9×	600 0.7×	178 0.4×	138 1.3×	71 0.7×	35	1.6k
Prudence Mutowo United Kingdom	6	1.5k 1.4×	1.2k 1.4×	324 0.7×	71 0.7×	278 2.8×	7	2.2k
Gerard Martínez-Rosell Spain	13	1.3k 1.2×	934 1.1×	444 1.0×	26 0.3×	142 1.4×	13	1.7k
Inbal Halperin United States	9	1.4k 1.3×	629 0.7×	383 0.9×	73 0.7×	80 0.8×	9	1.8k
Elena Cibrián–Uhalte Germany	7	1.1k 1.0×	1.1k 1.3×	314 0.7×	46 0.4×	261 2.7×	9	1.8k
Wenhua Kuang China	12	731 0.7×	525 0.6×	145 0.3×	69 0.7×	39 0.4×	34	966
Suzanne Brewerton United Kingdom	12	869 0.8×	487 0.6×	164 0.4×	52 0.5×	125 1.3×	20	1.2k
Lora Mak United Kingdom	5	829 0.8×	836 1.0×	222 0.5×	27 0.3×	179 1.8×	6	1.2k
Andras Boeszoermenyi United States	19	923 0.9×	254 0.3×	153 0.3×	36 0.3×	74 0.8×	28	1.5k

Countries citing papers authored by Miha Škalič

Since Specialization

Citations

This map shows the geographic impact of Miha Škalič'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 Miha Škalič with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Miha Škalič more than expected).

Fields of papers citing papers by Miha Škalič

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Miha Škalič. 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 Miha Škalič. The network helps show where Miha Škalič may publish in the future.

Co-authorship network of co-authors of Miha Škalič

This figure shows the co-authorship network connecting the top 25 collaborators of Miha Škalič. A scholar is included among the top collaborators of Miha Škalič 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 Miha Škalič. Miha Škalič is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

Aljayyoussi, Ghaith, et al.. (2025). Predicting Pharmacokinetics in Rats Using Machine Learning: A Comparative Study Between Empirical, Compartmental, and PBPK ‐Based Approaches. Clinical and Translational Science. 18(3). e70150–e70150. 5 indexed citations

Borghardt, Jens Markus, et al.. (2024). Multi‐Task ADME/PK prediction at industrial scale: leveraging large and diverse experimental datasets**. Molecular Informatics. 43(10). e202400079–e202400079. 4 indexed citations

Jiménez-Luna, José, Miha Škalič, & Nils Weskamp. (2022). Benchmarking Molecular Feature Attribution Methods with Activity Cliffs. Journal of Chemical Information and Modeling. 62(2). 274–283. 18 indexed citations

Jiménez-Luna, José, Miha Škalič, Nils Weskamp, & Gisbert Schneider. (2021). Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment. Journal of Chemical Information and Modeling. 61(3). 1083–1094. 57 indexed citations

Škalič, Miha, Daniela Fracassetti, Concetta Compagno, et al.. (2020). Transcriptomics unravels the adaptive molecular mechanisms of Brettanomyces bruxellensis under SO2 stress in wine condition. Food Microbiology. 90. 103483–103483. 14 indexed citations

Škalič, Miha, José Jiménez-Luna, Davide Sabbadin, & Gianni De Fabritiis. (2019). Shape-Based Generative Modeling for de Novo Drug Design. Journal of Chemical Information and Modeling. 59(3). 1205–1214. 150 indexed citations

Škalič, Miha, Davide Sabbadin, Boris Sattarov, Simone Sciabola, & Gianni De Fabritiis. (2019). From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design. Molecular Pharmaceutics. 16(10). 4282–4291. 92 indexed citations

Trincado, Juan L., Juan Carlos Entizne, Gerald Hysenaj, et al.. (2018). SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions. Genome biology. 19(1). 40–40. 355 indexed citations breakdown →

Škalič, Miha, Alejandro Varela‐Rial, José Jiménez-Luna, Gerard Martínez-Rosell, & Gianni De Fabritiis. (2018). LigVoxel: inpainting binding pockets using 3D-convolutional neural networks. Bioinformatics. 35(2). 243–250. 46 indexed citations

10.

Jiménez-Luna, José, Miha Škalič, Gerard Martínez-Rosell, & Gianni De Fabritiis. (2018). K_DEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks. Journal of Chemical Information and Modeling. 58(2). 287–296. 637 indexed citations breakdown →

11.

Škalič, Miha, Gerard Martínez-Rosell, José Jiménez-Luna, & Gianni De Fabritiis. (2018). PlayMolecule BindScope: large scale CNN-based virtual screening on the web. Bioinformatics. 35(7). 1237–1238. 42 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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