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Ozlem Ozmen Garibay on Muck Rack

Ozlem Ozmen Garibay

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As seen in: Taylor & Francis Online, BioRxiv
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Articles

Computer Science > Machine Learning

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By Mohammad Jahid Ibna, Ali Khodabandeh Yalabadi, Ozlem Ozmen Garibay
| arXiv
[Submitted on 12 May 2026] Title:ConRetroBert: EMA Stabilized Dual Encoders for Template-Based Single-Step Retrosynthesis View a PDF of the paper titled ConRetroBert: EMA Stabilized Dual Encoders for Template-Based Single-Step Retrosynthesis, by Mohammad Jahid Ibna Basher and 3 other authors View PDF HTML (experimental) Abstract:Template based single step retrosynthesis predicts reactants by selecting and applying an explicit reaction template, making each prediction traceable to a chemical...
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Six Human-Centered Artificial Intelligence Grand Challenges

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By Ozlem Ozmen Garibay, Brent Winslow, Salvatore Andolina, Margherita Antona
| Taylor & Francis Online
4.2.1. Case study: social robots How, then, could these concerns play out in real-time? This of course depends on the application of the AI.
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Interpretable and Generalizable Attention-Based Model for Predicting Drug-Target Interaction Using 3D Structure of Protein Binding Sites: SARS-CoV-2 Case Study and in-Lab Validation

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By Mehdi Yazdani-Jahromi, Niloofar Yousefi, Aida Tayebi, Ozlem Ozmen Garibay
| BioRxiv
Abstract In this study, we introduce and implement an interpretable graph-based deep learning prediction model, which utilizes protein binding sites along with self-attention to learn which protein binding sites interact with a given ligand. Our proposed model enables interpretability by identifying the protein binding sites that contribute the most towards the Drug-Target Interaction. Results on three benchmark datasets show improved performance compared to previous graph-based models.
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