Author

AkshatKumar Nigam

Stanford UniversityORCID 0000-0002-5152-2082Unclaimed 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· 2 Research Focus:drug response / drug discovery· 2 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Christoph GorgullaiD· 2 shared Christopher SeckeriD· 2 shared Konstantin FackeldeyiD· 2 shared Abhilash JayarajiD· 1 shared Abhishek Kumar· 1 shared Aditya KumariD· 1 shared Alán Aspuru‐GuzikiD· 1 shared Alexander HassoniD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Christoph GorgullaORCID

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Author profile →

3 papers

Recent·Most cited

Evolutionary exploration of drug-like chemical space utilizing generative AI and virtual screening

Christopher Secker, Philipp F. Secker, Fatih Yergöz, M. Oezguer Celik, Surahit Chewle, Maria Phuong Nga Le, Mustafa Masoud, Steffen Christgau, Marcus Weber, Christoph Gorgulla, AkshatKumar Nigam, Robert Pollice, Christof Schütte, Konstantin Fackeldey

This paper presents an evolutionary algorithm framework combining generative AI and virtual screening to efficiently explore large chemical spaces and identify novel drug candidates.

bioRxiv · Mar 30, 2026 · PreprintBioinformatics

Prediction and design of transcriptional repressor domains with large-scale mutational scans and deep learning

Raeline Valbuena, AkshatKumar Nigam, Josh Tycko, Peter Suzuki, Kaitlyn K. Spees, Aradhana, Sophia M. Arana, Peter P. Du, Roshni A. Patel, Lacramioara Bintu, Anshul Bharat Kundaje, Michael Cory Bassik

The paper predicts and designs transcriptional repressor domains using mutational scans and a deep learning model called TENet, identifying functional variants and enabling synthetic design.

bioRxiv · Sep 21, 2024 · PreprintGenomics

AI-Enhanced Adaptive Virtual Screening Platform Enabling Exploration of 69 Billion Molecules Discovers Structurally Validated FSP1 Inhibitors

Domiziana Cecchini, AkshatKumar Nigam, Ming Tang, Joana Reis, Matt Koop, Andrea Gottinger, Callum Robert Nicoll, Yao Wang, Abhilash Jayaraj, Suleyman Selim Cinaroglu, Ricarda Törner, Yehor S. Malets, Minko Gehev, Krishna M. Padmanabha Das, Kelly Churion, , Christoph Gorgulla

AdaptiveFlow enables large-scale virtual screening of 69 billion molecules to discover validated FSP1 inhibitors, improving efficiency and scalability in drug discovery.

bioRxiv · Apr 25, 2023 · PreprintBioinformatics