Homa MohammadiPeyhani

ConformAb designs antibody sequences that maintain CDR canonical conformations, improving binding affinity without target-specific data.

Property Enhancer optimizes antibody properties using a data-efficient framework, achieving significant affinity gains in both in silico and in vitro tests.

deepNGS Navigator uses deep contrastive learning to visualize and analyze antibody sequence datasets, revealing clonal relationships and evolutionary patterns.

This paper introduces a machine learning framework for therapeutic antibody design that integrates computational methods with experimental validation, resulting in significantly improved binding affinity for multiple targets.

BridgIT+ predicts protein-reaction associations by leveraging enzyme promiscuity and sequence profiles, outperforming existing methods on E. coli and predicted reactions.

NICEgame identifies and resolves metabolic gaps in genome-scale models by suggesting alternative reactions and genes, improving gene essentiality predictions in E. coli.

ARBRE predicts biosynthetic pathways to industrially important aromatic compounds using a comprehensive reaction network and computational tools.

BridgIT assigns enzyme sequences to orphan and novel reactions by comparing reactive site structures, predicting sequences for 93% of reactions with three connecting bonds and nearly all with six.