Supporting Data for Dynamic Scapping of RTK Domains: Computational Modeling of Natural Products as Dual Modulators of EGFR and VEGFR Signaling in Breast Cancer.

This dataset contains the natural product library, docking input files, redocked poses, and optimized ligand geometries supporting the findings in the manuscript submitted to Journal of Chemical Information and Modeling. A PDF describing the data will be added in a later version.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-08
DOI
https://doi.org/10.5281/zenodo.15366406
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Supporting Data for Dynamic Scapping of RTK Domains: Computational Modeling of Natural Products as Dual Modulators of EGFR and VEGFR Signaling in Breast Cancer.

Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
article

Supporting Data for Dynamic Scapping of RTK Domains: Computational Modeling of Natural Products as Dual Modulators of EGFR and VEGFR Signaling in Breast Cancer.

article en

Abstract

This dataset contains the natural product library, docking input files, redocked poses, and optimized ligand geometries supporting the findings in the manuscript submitted to Journal of Chemical Information and Modeling. A PDF describing the data will be added in a later version.

Zenodo (CERN European Organization for Nuclear Research)
University of Johannesburg (ZA)
Openalex Percentile: Top 56%
Computational Drug Discovery Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.