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Rafal Kurczab
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2020 – today
- 2024
- [j11]Arkadiusz Leniak
, Wojciech Pietrus
From NMR to AI: Designing a Novel Chemical Representation to Enhance Machine Learning Predictions of Physicochemical Properties. J. Chem. Inf. Model. 64(8): 3302-3321 (2024) - 2022
- [c1]Maciej Wolczyk, Magdalena Proszewska, Lukasz Maziarka, Maciej Zieba, Patryk Wielopolski, Rafal Kurczab, Marek Smieja:
PluGeN: Multi-Label Conditional Generation from Pre-trained Models. AAAI 2022: 8647-8656 - 2021
- [j10]Stefan Mordalski
2D SIFt: a matrix of ligand-receptor interactions. J. Cheminformatics 13(1): 66 (2021) - [j9]Dawid Warszycki, Lukasz Struski, Marek Smieja, Rafal Kafel, Rafal Kurczab
Pharmacoprint: A Combination of a Pharmacophore Fingerprint and Artificial Intelligence as a Tool for Computer-Aided Drug Design. J. Chem. Inf. Model. 61(10): 5054-5065 (2021) - [i2]Maciej Wolczyk, Magdalena Proszewska, Lukasz Maziarka, Maciej Zieba, Patryk Wielopolski, Rafal Kurczab, Marek Smieja:
PluGeN: Multi-Label Conditional Generation From Pre-Trained Models. CoRR abs/2109.09011 (2021) - [i1]Dawid Warszycki, Lukasz Struski, Marek Smieja, Rafal Kafel, Rafal Kurczab:
Pharmacoprint - a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design. CoRR abs/2110.01339 (2021)
2010 – 2019
- 2018
- [j8]Rafal Kurczab
Salt Bridge in Ligand-Protein Complexes - Systematic Theoretical and Statistical Investigations. J. Chem. Inf. Model. 58(11): 2224-2238 (2018) - 2014
- [j7]Rafal Kurczab
The influence of negative training set size on machine learning-based virtual screening. J. Cheminformatics 6(1): 32 (2014) - [j6]Mari Gabrielsen
Identification of Novel Serotonin Transporter Compounds by Virtual Screening. J. Chem. Inf. Model. 54(3): 933-943 (2014) - 2013
- [j5]Sabina Smusz
The influence of the inactives subset generation on the performance of machine learning methods. J. Cheminformatics 5: 17 (2013) - [j4]Sabina Smusz
The influence of hashed fingerprints density on the machine learning methods performance. J. Cheminformatics 5(S-1): 25 (2013) - [j3]Rafal Kurczab, Sabina Smusz
The influence of training actives/inactives ratio on machine learning performance. J. Cheminformatics 5(S-1): 30 (2013) - [j2]Rafal Kurczab
New Strategy for Receptor-Based Pharmacophore Query Construction: A Case Study for 5-HT7 Receptor Ligands. J. Chem. Inf. Model. 53(12): 3233-3243 (2013) - 2011
- [j1]Rafal Kurczab
Evaluation of different machine learning methods for ligand-based virtual screening. J. Cheminformatics 3(S-1): 41 (2011)
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