Author

Neil Mody

ORCID 0000-0003-2260-7658Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Assay:protein sequence· 2 Approach:deep learning· 1 Approach:graph neural network· 1 Organism:human· 1 Assay:protein structure· 1 Research Focus:protein structure prediction· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Gilad KaplaniD· 2 shared Maryam PouryahyaiD· 2 shared A N M Nafiz AbeeriD· 1 shared Alison Hinton· 1 shared Amber N. LeeiD· 1 shared Andrew B. DippeliD· 1 shared Bismark AmofahiD· 1 shared Chacko Chakiath· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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2 shared papersBioinformatics

Gilad KaplanORCID

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2 papers

Recent·Most cited

BioinformaticsmAbsPeer-reviewedJul 8, 2026

Predicting Antibody Self-Association with Sequence Structure Fusion Models: The Central Role of CSI-BLI in Early Developability Screening

Shafayat Ahmed, Federico Devalle, Lauren Leisen…·Maryam Pouryahya

The paper develops a sequence-structure fusion model to predict antibody self-association using CSI-BLI data, achieving high performance in distinguishing high and low self-interacting clones.

1 citation

humanprotein sequence

𝕏 PostBluesky

BioinformaticsbioRxivPreprintFeb 2, 2025

Accelerating Antibody Development: Sequence and Structure-Based Models for Predicting Developability Properties through Size Exclusion Chromatography

A N M Nafiz Abeer, Mehdi Boroumand, Isabelle Sermadiras…·Maryam Pouryahya

This study compares sequence and structure-based models for predicting antibody developability properties using SEC assays, identifying the most effective in silico approach.

DOI 10.1101/2025.02.02.636157

protein sequencemachine learning (classical)

𝕏 PostBluesky