Author

Chengqi Xu

Cornell UniversityORCID 0000-0003-3361-8117Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:drug response / drug discovery· 3 Organism:human· 2 Assay:bulk RNA-seq· 1 Approach:large language model· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Heng PaniD· 3 shared Krishna C. BulusuiD· 3 shared Olivier ElementoiD· 3 shared Ari M. MelnickiD· 1 shared Ben S. SiddersiD· 1 shared Ilkay UsiD· 1 shared Jake Cohen-SettoniD· 1 shared Jiaqi Xu· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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3 shared papersBioinformatics

Heng PanORCID

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJan 30, 2026

BEACON: predicting side effects and therapeutics outcomes to drugs by Bridging knowlEdge grAph with CONtextual language model

Chengqi Xu, Jiaqi Xu, Krishna C. Bulusu…·Olivier Elemento

BEACON predicts drug side effects and therapeutic outcomes by integrating biomedical knowledge graphs with contextual language models, achieving high accuracy in drug sensitivity and drug-drug interaction predictions.

DOI 10.64898/2026.01.29.702277

humandeep learning

𝕏 PostBluesky

BioinformaticsbioRxivPreprintDec 6, 2024

DDI-GPT: Explainable Prediction of Drug-Drug Interactions using Large Language Models enhanced with Knowledge Graphs

Chengqi Xu, Krishna C. Bulusu, Heng Pan, Olivier Elemento

DDI-GPT predicts drug-drug interactions using a large language model enhanced with knowledge graphs, achieving high accuracy and providing explainable insights into interaction mechanisms.

9 citations

deep learninglarge language model

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BioinformaticsbioRxivPreprintNov 4, 2024

PAIRWISE: Deep Learning-based Prediction of Effective Personalized Drug Combinations in Cancer

Chengqi Xu, Ilkay Us, Jake Cohen-Setton…·Krishna C. Bulusu

PAIRWISE predicts effective personalized drug combinations in cancer using deep learning and transcriptome data, achieving high AUROC scores in validation tests.

2 citations

humanbulk RNA-seq

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