ArticleJournal of cheminformatics2024
QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool.
Article in Journal of cheminformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
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Who cites it
7 citing papers in PubMed.
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Predictive modeling of influenza strain drugs using temperature-based topological indices and regression analysis via multi-criteria decision making techniques.Scientific reports · 2026Article
- Fingerprint-Based Machine Learning for SARS-CoV-2 and MERS-CoVJournal of chemical information and modeling · 2025Article
- Prediction model for chemical explosion consequences via multimodal feature fusion.Journal of cheminformatics · 2025Article
- Toward Assay-Aware Bioactivity Model(er)s: Getting a Grip on Biological Context.Journal of chemical information and modeling · 2025Article
- Integrating Pharmacokinetics and Quantitative Systems Pharmacology Approaches in Generative Drug Design.Journal of chemical information and modeling · 2025Article
- MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery.Journal of chemical information and modeling · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. In this work, we introduce QSPRpred, a toolkit for analysis of bioactivity data sets and QSPR modelling, which attempts to address the aforementioned challenges. QSPRpred's modular Python API enables users to intuitively describe different parts of a modelling workflow using a plethora of pre-implemented components, but also integrates customized implementations in a "plug-and-play" manner. QSPRpred data sets and models are directly serializable, which means they can be readily reproduced and put into operation after training as the models are saved with all required data pre-processing steps to make predictions on new compounds directly from SMILES strings. The general-purpose character of QSPRpred is also demonstrated by inclusion of support for multi-task and proteochemometric modelling. The package is extensively documented and comes with a large collection of tutorials to help new users. In this paper, we describe all of QSPRpred's functionalities and also conduct a small benchmarking case study to illustrate how different components can be leveraged to compare a diverse set of models. QSPRpred is fully open-source and available at https://github.com/CDDLeiden/QSPRpred .Scientific ContributionQSPRpred aims to provide a complex, but comprehensive Python API to conduct all tasks encountered in QSPR modelling from data preparation and analysis to model creation and model deployment. In contrast to similar packages, QSPRpred offers a wider and more exhaustive range of capabilities and integrations with many popular packages that also go beyond QSPR modelling. A significant contribution of QSPRpred is also in its automated and highly standardized serialization scheme, which significantly improves reproducibility and transferability of models.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.