Pedro J. Ballester

7.4k total citations · 1 hit paper
71 papers, 4.4k citations indexed

About

Pedro J. Ballester is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Pedro J. Ballester has authored 71 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Computational Theory and Mathematics, 44 papers in Molecular Biology and 19 papers in Materials Chemistry. Recurrent topics in Pedro J. Ballester's work include Computational Drug Discovery Methods (44 papers), Protein Structure and Dynamics (20 papers) and Machine Learning in Materials Science (14 papers). Pedro J. Ballester is often cited by papers focused on Computational Drug Discovery Methods (44 papers), Protein Structure and Dynamics (20 papers) and Machine Learning in Materials Science (14 papers). Pedro J. Ballester collaborates with scholars based in France, United Kingdom and Hong Kong. Pedro J. Ballester's co-authors include John B. O. Mitchell, Hongjian Li, W. Graham Richards, Kwong‐Sak Leung, Tjama Tjivikua, Julius Rebek, Man‐Hon Wong, Maciej Wójcikowski, Paweł Siedlecki and Gang Lü and has published in prestigious journals such as Nature, Journal of the American Chemical Society and Chemical Society Reviews.

In The Last Decade

Pedro J. Ballester

70 papers receiving 4.3k 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.

A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

2010 618 citations Pedro J. Ballester et al. Bioinformatics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pedro J. Ballester France	34	2.9k	2.9k	1.1k	406	322	71	4.4k
David Ryan Koes United States	25	2.3k 0.8×	2.0k 0.7×	804 0.7×	351 0.9×	482 1.5×	73	3.7k
Hongming Chen Sweden	29	2.7k 0.9×	3.6k 1.3×	2.1k 1.9×	348 0.9×	375 1.2×	69	5.1k
Paul Czodrowski Germany	21	2.5k 0.9×	1.3k 0.4×	738 0.7×	212 0.5×	318 1.0×	41	4.2k
Gianni De Fabritiis Spain	40	5.4k 1.8×	2.3k 0.8×	2.5k 2.2×	334 0.8×	388 1.2×	119	7.9k
Chang‐Yu Hsieh China	29	2.1k 0.7×	2.3k 0.8×	1.2k 1.1×	321 0.8×	574 1.8×	128	5.0k
W. Patrick Walters United States	27	3.2k 1.1×	3.5k 1.2×	1.2k 1.1×	549 1.4×	1.1k 3.4×	54	5.6k
Yvonne Light United Kingdom	16	4.4k 1.5×	3.1k 1.1×	711 0.6×	671 1.7×	527 1.6×	17	6.1k
Huanxiang Liu China	42	3.1k 1.1×	2.1k 0.7×	1.0k 0.9×	284 0.7×	944 2.9×	307	6.7k
Gerard J. P. van Westen Netherlands	34	2.1k 0.7×	1.5k 0.5×	442 0.4×	299 0.7×	190 0.6×	130	3.2k
Michael M. Mysinger United States	8	2.8k 1.0×	2.5k 0.9×	684 0.6×	514 1.3×	558 1.7×	8	4.4k

Countries citing papers authored by Pedro J. Ballester

Since Specialization

Citations

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

Fields of papers citing papers by Pedro J. Ballester

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pedro J. Ballester. 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 Pedro J. Ballester. The network helps show where Pedro J. Ballester may publish in the future.

Co-authorship network of co-authors of Pedro J. Ballester

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Ballester, Pedro J., et al.. (2025). UMAP-based clustering split for rigorous evaluation of AI models for virtual screening on cancer cell lines*. Journal of Cheminformatics. 17(1). 94–94. 3 indexed citations

Ghislat, Ghita, et al.. (2025). A pan-cancer, pan-treatment model for predicting drug responses from patient-derived xenografts. NAR Genomics and Bioinformatics. 7(3). lqaf111–lqaf111.

Ballester, Pedro J., et al.. (2024). Graph neural networks are promising for phenotypic virtual screening on cancer cell lines. Biology Methods and Protocols. 9(1). bpae065–bpae065. 3 indexed citations

Bruyns‐Haylett, Michael, et al.. (2023). A machine learning approach to predict cellular uptake of pBAE polyplexes. Biomaterials Science. 11(17). 5797–5808. 15 indexed citations

Tran‐Nguyen, Viet‐Khoa, Muhammad Junaid, Saw Simeon, & Pedro J. Ballester. (2023). A practical guide to machine-learning scoring for structure-based virtual screening. Nature Protocols. 18(11). 3460–3511. 37 indexed citations

Ghislat, Ghita, et al.. (2023). Large-Scale Machine Learning Analysis Reveals DNA Methylation and Gene Expression Response Signatures for Gemcitabine-Treated Pancreatic Cancer. SHILAP Revista de lepidopterología. 4. 108–108. 10 indexed citations

Ballester, Pedro J., et al.. (2023). On the Best Way to Cluster NCI-60 Molecules. Biomolecules. 13(3). 498–498. 13 indexed citations

Tran‐Nguyen, Viet‐Khoa, Saw Simeon, Muhammad Junaid, & Pedro J. Ballester. (2022). Structure-based virtual screening for PDL1 dimerizers: Evaluating generic scoring functions. SHILAP Revista de lepidopterología. 4. 206–210. 12 indexed citations

Ghislat, Ghita, et al.. (2021). A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling. Briefings in Bioinformatics. 22(6). 7 indexed citations

10.

Canals, Vincent, et al.. (2021). Using Stochastic Computing for Virtual Screening Acceleration. Electronics. 10(23). 2981–2981. 1 indexed citations

11.

Naulaerts, Stefan, et al.. (2021). Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles. Biomedicines. 9(10). 1319–1319. 20 indexed citations

12.

Ghislat, Ghita, Christophe Verthuy, Pedro J. Ballester, et al.. (2021). NF-κB–dependent IRF1 activation programs cDC1 dendritic cells to drive antitumor immunity. Science Immunology. 6(61). 77 indexed citations

13.

Grand, Marion Le, Laurent Hoffer, S. Betzi, et al.. (2020). In silico molecular target prediction unveils mebendazole as a potent MAPK14 inhibitor. Molecular Oncology. 14(12). 3083–3099. 22 indexed citations

14.

Li, Hongjian, et al.. (2020). Machine‐learning scoring functions for structure‐based drug lead optimization. Wiley Interdisciplinary Reviews Computational Molecular Science. 10(5). 112 indexed citations

15.

Ballester, Pedro J., et al.. (2020). The impact of compound library size on the performance of scoring functions for structure-based virtual screening. Briefings in Bioinformatics. 22(3). 30 indexed citations

16.

Li, Hongjian, et al.. (2020). Machine‐learning scoring functions for structure‐based virtual screening. Wiley Interdisciplinary Reviews Computational Molecular Science. 11(1). 113 indexed citations

17.

Li, Hongjian, Jiangjun Peng, Pavel Sidorov, et al.. (2019). Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data. Bioinformatics. 35(20). 3989–3995. 70 indexed citations

18.

Li, Hongjian, Jiangjun Peng, Yee Leung, et al.. (2018). The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction. Biomolecules. 8(1). 12–12. 39 indexed citations

19.

Ziehm, Matthias, Satwant Kaur, Dobril Ivanov, et al.. (2017). Drug repurposing for aging research using model organisms. Aging Cell. 16(5). 1006–1015. 28 indexed citations

20.

Li, Hongjian, Kwong‐Sak Leung, Man‐Hon Wong, & Pedro J. Ballester. (2015). Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest. Molecules. 20(6). 10947–10962. 77 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.

Explore authors with similar magnitude of impact