Rafal Kurczab

According to our database, Rafal Kurczab authored at least 12 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

From NMR to AI: Designing a Novel Chemical Representation to Enhance Machine Learning Predictions of Physicochemical Properties.

Arkadiusz Leniak, Wojciech Pietrus, Rafal Kurczab

J. Chem. Inf. Model., 2024

2022

PluGeN: Multi-Label Conditional Generation from Pre-trained Models.

Maciej Wolczyk, Magdalena Proszewska, Lukasz Maziarka, Maciej Zieba, Patryk Wielopolski, Rafal Kurczab, Marek Smieja

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Pharmacoprint: A Combination of a Pharmacophore Fingerprint and Artificial Intelligence as a Tool for Computer-Aided Drug Design.

Dawid Warszycki, Lukasz Struski, Marek Smieja, Rafal Kafel, Rafal Kurczab

J. Chem. Inf. Model., 2021

2D SIFt: a matrix of ligand-receptor interactions.

Stefan Mordalski, Agnieszka Wojtuch, Igor T. Podolak, Rafal Kurczab, Andrzej J. Bojarski

J. Cheminformatics, 2021

2018

Salt Bridge in Ligand-Protein Complexes - Systematic Theoretical and Statistical Investigations.

Rafal Kurczab, Pawel Sliwa, Krzysztof Rataj, Rafal Kafel, Andrzej J. Bojarski

J. Chem. Inf. Model., 2018

2014

Identification of Novel Serotonin Transporter Compounds by Virtual Screening.

Mari Gabrielsen, Rafal Kurczab, Agata Siwek, Malgorzata Wolak, Aina Westrheim Ravna, Kurt Kristiansen, Irina Kufareva, Ruben Abagyan, Gabriel Nowak, Zdzislaw Chilmonczyk, Ingebrigt Sylte, Andrzej J. Bojarski

J. Chem. Inf. Model., 2014

The influence of negative training set size on machine learning-based virtual screening.

Rafal Kurczab, Sabina Smusz, Andrzej J. Bojarski

J. Cheminformatics, 2014

2013

New Strategy for Receptor-Based Pharmacophore Query Construction: A Case Study for 5-HT₇ Receptor Ligands.

Rafal Kurczab, Andrzej J. Bojarski

J. Chem. Inf. Model., 2013

The influence of hashed fingerprints density on the machine learning methods performance.

Sabina Smusz, Rafal Kurczab, Andrzej J. Bojarski

J. Cheminformatics, 2013

The influence of the inactives subset generation on the performance of machine learning methods.

Sabina Smusz, Rafal Kurczab, Andrzej J. Bojarski

J. Cheminformatics, 2013

The influence of training actives/inactives ratio on machine learning performance.

Rafal Kurczab, Sabina Smusz, Andrzej J. Bojarski

J. Cheminformatics, 2013

2011

Evaluation of different machine learning methods for ligand-based virtual screening.

Rafal Kurczab, Sabina Smusz, Andrzej J. Bojarski

J. Cheminformatics, 2011