Equivariant 3D-conditional diffusion model for molecular linker design
Most Cited Paper
in Computational Drug Discovery Methods
18 Months Post-Publication
Class of 2026
“Equivariant 3D-conditional diffusion model for molecular linker design”
A research intelligence initiative by Pepkio
pri.pepkio.com/top-papers/computational-drug-discovery-methods/2026/
Topics
DiffLinker · Equivariant diffusion models · three-dimensional conditional diffusion · Linker design · fragment-based drug discovery · disconnected molecular fragments · arbitrary number of fragments · automatic atom placement · attachment point prediction · Protein pocket conditioning · Synthetic accessibility · Chemical diversity · De novo molecular design · equivariant neural networks · Structure-based drug design
Share
Cite this ranking
Pepkio Research Index (PRI). Topics and Trends in Most Cited Computational Drug Discovery Methods Papers, Class of 2026. https://pri.pepkio.com/top-papers/computational-drug-discovery-methods/2026. Accessed 2026-07-22. Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459