Sabina Podlewska

According to our database, Sabina Podlewska authored at least 22 papers between 2011 and 2025.

Collaborative distances:

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

2025

PROFIS: Design of Target-Focused Libraries by Probing Continuous Fingerprint Space with Recurrent Neural Networks.

Hubert Rybka, Tomasz Danel, Sabina Podlewska

J. Chem. Inf. Model., 2025

2024

ADMET-PrInt: Evaluation of ADMET Properties: Prediction and Interpretation.

Ewelina Jamrozik, Marek Smieja, Sabina Podlewska

J. Chem. Inf. Model., March, 2024

2023

Extended study on atomic featurization in graph neural networks for molecular property prediction.

Agnieszka Wojtuch, Tomasz Danel, Sabina Podlewska, Lukasz Maziarka

J. Cheminformatics, December, 2023

Generative Models Should at Least Be Able to Design Molecules That Dock Well: A New Benchmark.

Tobiasz Cieplinski, Tomasz Danel, Sabina Podlewska, Stanislaw Jastrzebski

J. Chem. Inf. Model., June, 2023

Docking-based generative approaches in the search for new drug candidates.

Tomasz Danel, Jan Leski, Sabina Podlewska, Igor T. Podolak

CoRR, 2023

2022

Low cost prediction of probability distributions of molecular properties for early virtual screening.

Jarek Duda, Sabina Podlewska

CoRR, 2022

2021

How can SHAP values help to shape metabolic stability of chemical compounds?

Agnieszka Wojtuch, Rafal Jankowski, Sabina Podlewska

J. Cheminformatics, 2021

Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction.

Agnieszka Pocha, Tomasz Danel, Sabina Podlewska, Jacek Tabor, Lukasz Maziarka

Proceedings of the International Joint Conference on Neural Networks, 2021

2020

Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening.

Stanislaw Jastrzebski, Maciej Szymczak, Agnieszka Pocha, Stefan Mordalski, Jacek Tabor, Andrzej J. Bojarski, Sabina Podlewska

J. Chem. Inf. Model., 2020

We should at least be able to Design Molecules that Dock Well.

Tobiasz Cieplinski, Tomasz Danel, Sabina Podlewska, Stanislaw Jastrzebski

CoRR, 2020

Similar, or dissimilar, that is the question. How different are methods for comparison of compounds similarity?

Krzysztof Rajda, Sabina Podlewska

Comput. Biol. Chem., 2020

2019

Development of New Methods Needs Proper Evaluation - Benchmarking Sets for Machine Learning Experiments for Class A GPCRs.

Damian Lesniak, Sabina Podlewska, Stanislaw Jastrzebski, Igor Sieradzki, Andrzej J. Bojarski, Jacek Tabor

J. Chem. Inf. Model., 2019

2017

Creating the New from the Old: Combinatorial Libraries Generation with Machine-Learning-Based Compound Structure Optimization.

Sabina Podlewska, Wojciech M. Czarnecki, Rafal Kafel, Andrzej J. Bojarski

J. Chem. Inf. Model., 2017

2015

Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning Methods - A Case Study of Serotonin Receptors 5-HT₆ and 5-HT₇.

Sabina Smusz, Stefan Mordalski, Jagna Witek, Krzysztof Rataj, Rafal Kafel, Andrzej J. Bojarski

J. Chem. Inf. Model., 2015

Multiple conformational states in retrospective virtual screening - homology models vs. crystal structures: beta-2 adrenergic receptor case study.

Stefan Mordalski, Jagna Witek, Sabina Smusz, Krzysztof Rataj, Andrzej J. Bojarski

J. Cheminformatics, 2015

Robust optimization of SVM hyperparameters in the classification of bioactive compounds.

Wojciech M. Czarnecki, Sabina Podlewska, Andrzej J. Bojarski

J. Cheminformatics, 2015

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

Application of Structural Interaction Fingerpints (SIFts) into post-docking analysis - insight into activity and selectivity.

Jagna Witek, Krzysztof Rataj, Stefan Mordalski, Sabina Smusz, Tomasz Kosciólek, Andrzej J. Bojarski

J. Cheminformatics, 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