Machine learning-aided generative molecular design
Most Cited Paper
in Computational Drug Discovery Methods
18 Months Post-Publication
Class of 2026
“Machine learning-aided generative molecular design”
A research intelligence initiative by Pepkio
pri.pepkio.com/top-papers/computational-drug-discovery-methods/2026/
Topics
machine learning · De novo molecular design · De novo molecular design · De novo molecular design · Deep learning models · Chemical space exploration · SMILES · molecular property prediction · reinforcement learning · generative adversarial networks · variational autoencoders · recurrent neural networks · Molecular optimization · Drug-likeness · Synthetic accessibility · transfer learning
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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