Rafal Kurczab

Orcid: 0000-0002-9555-3905

According to our database1, Rafal Kurczab authored at least 15 papers between 2011 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2025
Developing a Hybrid Molecular Representation Combining Chemical Structure and MIR Spectral Data: A LogP Prediction Case Study.
J. Chem. Inf. Model., 2025

From NMR to AI: Do We Need <sup>1</sup>H NMR Experimental Spectra to Obtain High-Quality logD Prediction Models?
J. Chem. Inf. Model., 2025

From NMR to AI: Fusing <sup>1</sup>H and <sup>13</sup>C Representations for Enhanced QSPR Modeling.
J. Chem. Inf. Model., 2025

2024
From NMR to AI: Designing a Novel Chemical Representation to Enhance Machine Learning Predictions of Physicochemical Properties.
J. Chem. Inf. Model., 2024

2022
PluGeN: Multi-Label Conditional Generation from Pre-trained Models.
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.
J. Chem. Inf. Model., 2021

2D SIFt: a matrix of ligand-receptor interactions.
J. Cheminformatics, 2021

2018
Salt Bridge in Ligand-Protein Complexes - Systematic Theoretical and Statistical Investigations.
J. Chem. Inf. Model., 2018

2014
Identification of Novel Serotonin Transporter Compounds by Virtual Screening.
J. Chem. Inf. Model., 2014

The influence of negative training set size on machine learning-based virtual screening.
J. Cheminformatics, 2014

2013
New Strategy for Receptor-Based Pharmacophore Query Construction: A Case Study for 5-HT<sub>7</sub> Receptor Ligands.
J. Chem. Inf. Model., 2013

The influence of hashed fingerprints density on the machine learning methods performance.
J. Cheminformatics, 2013

The influence of the inactives subset generation on the performance of machine learning methods.
J. Cheminformatics, 2013

The influence of training actives/inactives ratio on machine learning performance.
J. Cheminformatics, 2013

2011
Evaluation of different machine learning methods for ligand-based virtual screening.
J. Cheminformatics, 2011


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