Author

Viswa Colluru

ORCID 0000-0002-6208-9288Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Assay:metabolomics· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1 Organism:plant (other)· 1

Frequent Co-Authors

David HealeyiD· 3 shared Biswapriya B. MisraiD· 2 shared Daniel Domingo‐FernándéziD· 2 shared Tobias KindiD· 2 shared Yojana GadiyaiD· 2 shared Abe Frandsen· 1 shared Abhishek PateliD· 1 shared Andre Schaum· 1 shared

Activity by Year

2022-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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3 shared papersBioinformatics

David HealeyORCID

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3 papers

Recent·Most cited

BioinformaticsGigaSciencePeer-reviewedJan 1, 2025

Defining the limits of plant chemical space: challenges and estimations

Chloe Engler Hart, Yojana Gadiya, Tobias Kind…·Daniel Domingo‐Fernándéz

The paper estimates the size of plant chemical space using metabolomics and literature data, suggesting it may span millions of unique compounds, most of which remain unexplored.

12 citations

plant (other)metagenomics

BioinformaticsbioRxivPreprintOct 9, 2022

MS2Prop: A machine learning model that directly generates de novo predictions of drug-likeness of natural products from unannotated MS/MS spectra

Gennady Voronov, Rose Lightheart, Abe Frandsen…·Marvin S. Yu

MS2Prop predicts drug-likeness of natural products from unannotated MS/MS spectra without requiring chemical structure annotation.

18 citations

metabolomicsmachine learning (classical)

BioinformaticsPLoS Computational BiologyPeer-reviewedFeb 25, 2022

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

Daniel Domingo‐Fernándéz, Yojana Gadiya, Abhishek Patel…·Viswa Colluru

RPath prioritizes drugs for diseases by analyzing causal paths in knowledge graphs using drug-perturbed and disease-specific transcriptomic signatures, outperforming existing methods.

15 citations

bulk RNA-seqstatistical model