Author

Jesse A. Weller

University of Southern CaliforniaORCID 0000-0001-7184-4241Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Approach:deep learning· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:protein structure· 1

Frequent Co-Authors

Remo RohsiD· 2 shared Jinsen LiiD· 1 shared Yibei JiangiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

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3 top collaborators · 3 mutual co-author links

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2 shared papersBioinformatics

Remo RohsORCID

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2 papers

Recent·Most cited

BioinformaticsJournal of Chemical Information and ModelingPeer-reviewedJul 9, 2026

Circumventing the synthesizability problem in generative molecular design

Jesse A. Weller, Jinsen Li, Yibei Jiang, Remo Rohs

This paper introduces a model-guided virtual screening pipeline to improve synthesizability in generative drug design by combining SBDD models with chemical similarity search.

DOI 10.1021/acs.jcim.6c00998

machine learning (classical)drug response / drug discovery

𝕏 PostBluesky

BioinformaticsJournal of Chemical Information and ModelingPeer-reviewedJul 26, 2024

DrugHIVE: Target-specific spatial drug design and optimization with a hierarchical generative model

Jesse A. Weller, Remo Rohs

DrugHIVE is a deep generative model that accelerates drug design tasks by enabling fine-grained molecular generation and outperforming existing methods in speed and performance.

47 citationstop 1%

humanprotein structure

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