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Huseyin Tunc

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Articles

Improving Predictive Efficacy for Drug Resistance in Novel HIV-1 Protease Inhibitors through Transfer Learning Mechanisms

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By Huseyin Tunc, Sumeyye Yilmaz, Murat Sari, Seyfullah Kotil
| dx.doi.org
Machine Learning and Deep LearningOctober 11, 2024 * Huseyin Tunc Department of Biostatistics and Medical Informatics, School of Medicine, Bahcesehir University, Istanbul 34734, Turkey *Email: huseyin.tunc@bau.edu.tr Sumeyye Yilmaz Department of Mathematical Engineering, Istanbul Technical University, Istanbul 34469, Turkey Busra Nur Darendeli Kiraz Department of Bioengineering, Yildiz Technical University, Istanbul 34210, Turkey Murat Sari Department of Mathematical Engineering, Istanbul...
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Deep Learning-Driven Discovery of FDA-Approved BCL2 Inhibitors: In Silico Analysis Using a Deep Generative Model NeuralPlexer for Drug Repurposing in Cancer Treatment

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By Ehsan Sayyah, Huseyin Tunc, Serdar Durdagi
| BioRxiv
Abstract Finding strong inhibitors of the BCL2 target, which is essential for controlling apoptosis and ensuring the survival of cancer cells, has prompted research into FDA-approved drugs. This study uses an advanced deep generative model called NeuralPlexer to produce protein-ligand complex conformations one-by-one and perform in silico analysis.
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Multi-Drug Artificial Neural Network Models for Predicting HIV-1 RTI Resistance

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By Sumeyye Yilmaz, Huseyin Tunc, Murat Sari, Istanbul Technical
| BioRxiv
Abstract Drug resistance is a major barrier to effectively treating HIV/AIDS, necessitating the exploration of novel drugs and an understanding of resistance mechanisms. Genotypic and phenotypic tests, while common, are costly and time-consuming. To address these challenges, machine learning models that effectively generalize available data have been leveraged.
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