Author

Wen Torng

Google (United States)ORCID 0000-0002-8877-4785Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:drug response / drug discovery· 3 Approach:deep learning· 2 Research Focus:benchmarking· 1 Approach:experimental assay· 1 Approach:graph neural network· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:protein structure· 1

Frequent Co-Authors

Ian Watson· 2 shared Jianwen A. FengiD· 2 shared A. M. EdwardsiD· 1 shared Aiping DongiD· 1 shared Albina Bolotokova· 1 shared Aliakbar Khalili Yazdi· 1 shared Almagul Seitova· 1 shared Anthony D. KeefeiD· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBiochemistry

Ian Watson

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

A resource to enable chemical biology and drug discovery of WDR Proteins

Suzanne Z. Ackloo, Fengling Li, Magda Szewczyk, Almagul Seitova, P. Loppnau, Hong Zeng, Jin Hua Xu, Shabbir Ahmad, Yelena A. Arnautova, Arrash J. Baghaie, Serap Beldar, Albina Bolotokova, Paolo A. Centrella, Irene Chau, Matthew A. Clark, , C. H. Arrowsmith

The paper develops a resource for discovering ligands for WDR proteins using experimental and computational methods, leading to the identification of drug-like ligands for 9 out of 20 targets.

Journal of Medicinal Chemistry · Nov 4, 2024Peer-reviewedBiochemistry

A deep learning approach for the discovery of tumor-targeting small organic ligands from DNA-Encoded Chemical Libraries

Wen Torng, Ilaria Biancofiore, Sebastian Oehler, Jin Hua Xu, Jessica A. Xu, Ian Watson, Brenno Masina, Luca Prati, Nicholas Favalli, Gabriele Bassi, Dario Neri, Samuele Cazzamalli, Jianwen A. Feng

The paper uses deep learning to identify tumor-targeting ligands from DNA-encoded chemical libraries against CAIX, finding 70% of novel hits show submicromolar activity.

ACS Omega · Jul 6, 2023Peer-reviewedBioinformatics

Graph Convolutional Neural Networks for Predicting Drug-Target Interactions

Wen Torng, Russ Biagio Altman

The paper proposes a graph-convolutional framework for predicting protein-ligand interactions, achieving better or comparable performance to existing methods on benchmark datasets.

Journal of Chemical Information and Modeling · Oct 3, 2019Peer-reviewedBioinformatics