1.8k citations · 2.2k across the 5 of their papers we have counts for
5 papers
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…
WideDTA: prediction of drug-target binding affinity
Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
Motivation: Prediction of the interaction affinity between proteins and compounds is a major challenge in the drug discovery process. WideDTA is a deep-learning based prediction mo…
ChemBoost: A chemical language based approach for protein-ligand binding affinity prediction
Rıza Özçelik, Hakime Öztürk, Arzucan Özgür +1
Identification of high affinity drug-target interactions is a major research question in drug discovery. Proteins are generally represented by their structures or sequences. Howeve…
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…