activity
20202025
most citedA Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Prediction

14 citations · 26 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG2025

Look the Other Way: Designing 'Positive' Molecules with Negative Data via Task Arithmetic

Rıza Özçelik, Sarah de Ruiter, Francesca Grisoni

The scarcity of molecules with desirable properties (i.e., `positive' molecules) is an inherent bottleneck for generative molecule design. To sidestep such obstacle, here we propos…

q-bio.BM2025★ 5 cited

How Evaluation Choices Distort the Outcome of Generative Drug Discovery

Rıza Özçelik, Francesca Grisoni

"How to evaluate the de novo designs proposed by a generative model?" Despite the transformative potential of generative deep learning in drug discovery, this seemingly simple ques…

q-bio.BM2024★ 14 cited

A Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Prediction

Rıza Özçelik, Francesca Grisoni

Deep learning has significantly accelerated drug discovery, with 'chemical language' processing (CLP) emerging as a prominent approach. CLP learns from molecular string representat…

q-bio.BM2022★ 4 cited

Structure-based drug discovery with deep learning

Rıza Özçelik, Derek van Tilborg, José Jiménez-Luna +1

Artificial intelligence (AI) in the form of deep learning bears promise for drug discovery and chemical biology, , to predict protein structure and molecular bioacti…

q-bio.BM2022★ 3 cited

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…

cs.IR2020

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…

cs.CL2020

Data and Representation for Turkish Natural Language Inference

Emrah Budur, Rıza Özçelik, Tunga Güngör +1

Large annotated datasets in NLP are overwhelmingly in English. This is an obstacle to progress in other languages. Unfortunately, obtaining new annotated resources for each task in…