Author

Benjamin S. Suutari

Florida International University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Neuroscience· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1 Assay:protein structure· 1 Approach:statistical model· 1

Frequent Co-Authors

Antonia S. J. S. MeyiD· 2 shared Rohan GorantlaiD· 2 shared Álvaro Serrano‐MorrásiD· 1 shared Alžbeta KubincováiD· 1 shared Aryo Pradipta GemaiD· 1 shared Benjamin P. CossinsiD· 1 shared Boxing LiiD· 1 shared Daniel LevensteiniD· 1 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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2 shared papersBioinformatics

Antonia S. J. S. MeyORCID

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3 papers

Recent·Most cited

Learning Binding Affinities via Fine-tuning of Protein and Ligand Language Models

Rohan Gorantla, Aryo Pradipta Gema, Ian Xi Yang, Álvaro Serrano‐Morrás, Benjamin S. Suutari, Jordi Juárez‐Jiménez, Antonia S. J. S. Mey

This paper introduces BALM, a deep learning framework for predicting protein-ligand binding affinity using pre-trained language models, showing better performance than traditional methods in few-shot scenarios.

Journal of Chemical Information and Modeling · Oct 31, 2025Peer-reviewedBioinformatics

Synaptic homeostasis transiently leverages Hebbian mechanisms for a multiphasic response to inactivity

Simón D. Sun, Daniel Levenstein, Boxing Li, Nataniel J. Mandelberg, Nicolas Chenouard, Benjamin S. Suutari, Sandrine Sanchez, Guoling Tian, John Rinzel, György Buzsáki, Richard W. Tsien

The paper shows that synaptic homeostasis uses both Hebbian and homeostatic mechanisms in a nonmonotonic way to adapt to prolonged inactivity.

Cell Reports · Mar 19, 2024Peer-reviewedNeuroscience

Benchmarking active learning protocols for ligand binding affinity prediction

Rohan Gorantla, Alžbeta Kubincová, Benjamin S. Suutari, Benjamin P. Cossins, Antonia S. J. S. Mey

This study benchmarks active learning protocols for predicting ligand binding affinity using machine learning models and evaluates the impact of batch size and noise on performance.

Journal of Chemical Information and Modeling · Mar 6, 2024Peer-reviewedBioinformatics