José Jiménez-Luna

3.6k total citations · 3 hit papers
22 papers, 2.1k citations indexed

About

José Jiménez-Luna is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, José Jiménez-Luna has authored 22 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computational Theory and Mathematics, 15 papers in Molecular Biology and 13 papers in Materials Chemistry. Recurrent topics in José Jiménez-Luna's work include Computational Drug Discovery Methods (17 papers), Machine Learning in Materials Science (13 papers) and Protein Structure and Dynamics (10 papers). José Jiménez-Luna is often cited by papers focused on Computational Drug Discovery Methods (17 papers), Machine Learning in Materials Science (13 papers) and Protein Structure and Dynamics (10 papers). José Jiménez-Luna collaborates with scholars based in Spain, Switzerland and Germany. José Jiménez-Luna's co-authors include Gianni De Fabritiis, Gerard Martínez-Rosell, Miha Škalič, Gisbert Schneider, Stefan Doerr, Alexander Rose, Nils Weskamp, Francesca Grisoni, Davide Sabbadin and Kenneth Atz and has published in prestigious journals such as Nature Communications, Bioinformatics and Physical Chemistry Chemical Physics.

In The Last Decade

José Jiménez-Luna

22 papers receiving 2.0k 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.

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 →
DeepSite: protein-binding site predictor using 3D-convolutional neural networks

2017 500 citations José Jiménez-Luna, Stefan Doerr et al. Bioinformatics profile →
Artificial intelligence in drug discovery: recent advances and future perspectives

2021 243 citations José Jiménez-Luna, Francesca Grisoni et al. Expert Opinion on Drug Discovery profile →

Peers

Countries citing papers authored by José Jiménez-Luna

Since Specialization

Citations

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

Fields of papers citing papers by José Jiménez-Luna

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by José Jiménez-Luna. 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 José Jiménez-Luna. The network helps show where José Jiménez-Luna may publish in the future.

Co-authorship network of co-authors of José Jiménez-Luna

This figure shows the co-authorship network connecting the top 25 collaborators of José Jiménez-Luna. A scholar is included among the top collaborators of José Jiménez-Luna 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 José Jiménez-Luna. José Jiménez-Luna 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

#	Work	Indexed citations
1	Extracting medicinal chemistry intuition via preference machine learning 2023 ·Nature Communications ·(unknown), (unknown), Marwin Segler, Nikolaus Stiefl, José Jiménez-Luna	13
2	Explaining compound activity predictions with a substructure-aware loss for graph neural networks 2023 ·Journal of Cheminformatics ·(unknown), Raquel Rodríguez-Pérez, José Jiménez-Luna	8
3	Structure‐Based Drug Discovery with Deep Learning** 2023 ·ChemBioChem ·(unknown), Derek van Tilborg, José Jiménez-Luna, Francesca Grisoni	41
4	QMugs, quantum mechanical properties of drug-like molecules 2022 ·Scientific Data ·Clemens Isert, Kenneth Atz, José Jiménez-Luna, Gisbert Schneider	92
5	PlayMolecule Glimpse: Understanding Protein–Ligand Property Predictions with Interpretable Neural Networks 2022 ·Journal of Chemical Information and Modeling ·Alejandro Varela‐Rial, (unknown), Maciej Majewski, Stefan Doerr, (unknown), José Jiménez-Luna, Gianni De Fabritiis	13
6	Δ-Quantum machine-learning for medicinal chemistry 2022 ·Physical Chemistry Chemical Physics ·Kenneth Atz, Clemens Isert, (unknown), José Jiménez-Luna, Gisbert Schneider	42
7	Benchmarking Molecular Feature Attribution Methods with Activity Cliffs 2022 ·Journal of Chemical Information and Modeling ·José Jiménez-Luna, Miha Škalič, Nils Weskamp	18
8	Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment 2021 ·Journal of Chemical Information and Modeling ·José Jiménez-Luna, Miha Škalič, Nils Weskamp, Gisbert Schneider	57
9	Artificial intelligence in drug discovery: recent advances and future perspectives breakdown → 2021 ·Expert Opinion on Drug Discovery ·José Jiménez-Luna, Francesca Grisoni, Nils Weskamp, Gisbert Schneider	243
10	DeltaDelta neural networks for lead optimization of small molecule potency 2019 ·Chemical Science ·José Jiménez-Luna, Laura Pérez‐Benito, Gerard Martínez-Rosell, Simone Sciabola, Rubben Torella, Gary Tresadern, Gianni De Fabritiis	48
11	Shape-Based Generative Modeling for de Novo Drug Design 2019 ·Journal of Chemical Information and Modeling ·Miha Škalič, José Jiménez-Luna, Davide Sabbadin, Gianni De Fabritiis	150
12	PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks 2018 ·Journal of Chemical Information and Modeling ·José Jiménez-Luna, Davide Sabbadin, Alberto Cuzzolin, Gerard Martínez-Rosell, (unknown), John I. Manchester, José S. Duca, Gianni De Fabritiis	25
13	LigVoxel: inpainting binding pockets using 3D-convolutional neural networks 2018 ·Bioinformatics ·Miha Škalič, Alejandro Varela‐Rial, José Jiménez-Luna, Gerard Martínez-Rosell, Gianni De Fabritiis	46
14	K_DEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks breakdown → 2018 ·Journal of Chemical Information and Modeling ·José Jiménez-Luna, Miha Škalič, Gerard Martínez-Rosell, Gianni De Fabritiis	637
15	PlayMolecule BindScope: large scale CNN-based virtual screening on the web 2018 ·Bioinformatics ·Miha Škalič, Gerard Martínez-Rosell, José Jiménez-Luna, Gianni De Fabritiis	42
16	pyGPGO: Bayesian Optimization for Python 2017 ·The Journal of Open Source Software ·José Jiménez-Luna, Josep Ginebra	27
17	DeepSite: protein-binding site predictor using 3D-convolutional neural networks breakdown → 2017 ·Bioinformatics ·José Jiménez-Luna, Stefan Doerr, Gerard Martínez-Rosell, Alexander Rose, Gianni De Fabritiis	500
18	The Bondi metric and conformal motions 1987 ·Journal of Mathematical Physics ·A. Di Prisco, L. Herrera, José Jiménez-Luna, (unknown), Jesús Salinas	5
19	Junction conditions and the propagation of isometries in general relativity 1983 ·Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields ·L. Herrera, José Jiménez-Luna	23
20	Radiating Kerr–Newman metric 1979 ·Journal of Mathematical Physics ·(unknown), L. Herrera, José Jiménez-Luna	11

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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