1.8k citations · 2k across the 7 of their papers we have counts for
8 papers
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties
Asu Büşra Temizer, Gökçe Uludoğan, Rıza Özçelik +5
Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular…
Extraction of Medication Names from Twitter Using Augmentation and an Ensemble of Language Models
Igor Kulev, Berkay Köprü, Raul Rodriguez-Esteban +4
The BioCreative VII Track 3 challenge focused on the identification of medication names in Twitter user timelines. For our submission to this challenge, we expanded the available t…
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution
Yi Huang, Buse Giledereli, Abdullatif Köksal +2
Multi-label text classification is a challenging task because it requires capturing label dependencies. It becomes even more challenging when class distribution is long-tailed. Res…
DebiasedDTA: A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models
Rıza Özçelik, Alperen Bağ, Berk Atıl +3
Computational models that accurately predict the binding affinity of an input protein-chemical pair can accelerate drug discovery studies. These models are trained on available pro…
Vapur: A Search Engine to Find Related Protein-Compound Pairs in COVID-19 Literature
Abdullatif Köksal, Hilal Dönmez, Rıza Özçelik +2
Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a hu…
Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery
Hakime Öztürk, Arzucan Özgür, Philippe Schwaller +2
Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge. Advances in natural langu…
A novel methodology on distributed representations of proteins using their interacting ligands
Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
The effective representation of proteins is a crucial task that directly affects the performance of many bioinformatics problems. Related proteins usually bind to similar ligands.…
DeepDTA: Deep Drug-Target Binding Affinity Prediction
Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict…