14 citations · 23 across the 4 of their papers we have counts for
6 papers
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…
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…
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…
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…
Geometric Deep Learning on Molecular Representations
Kenneth Atz, Francesca Grisoni, Gisbert Schneider
Geometric deep learning (GDL), which is based on neural network architectures that incorporate and process symmetry information, has emerged as a recent paradigm in artificial inte…
Drug discovery with explainable artificial intelligence
José Jiménez-Luna, Francesca Grisoni, Gisbert Schneider
Deep learning bears promise for drug discovery, including advanced image analysis, prediction of molecular structure and function, and automated generation of innovative chemical e…