GenUI: Interactive and Extensible Open Source Software Platform for De Novo Molecular Generation

Liu, Xiaoyu; Svozil, Daniel; G, van Westen

doi:10.26434/chemrxiv.14685891.v1

Cited by 1 publication

(2 citation statements)

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“…Moreover, as the underlying pandas [55] dataframe can be accessed and edited, any further data analysis steps can be executed in this phase. For easy visualization and exploratory analyses of the dataset, integration with Scaffviz [56] is provided, which is described in more detail in the visualization section 2.2.6.…”

Section: Data Pre-processingmentioning

confidence: 99%

“…Moreover, correlation plots can be generated for multi and single-task regression models. In addition to the native ModelPlot, QSPRpred's MoleculeTable and QSPRModel instances can be used directly with the interactive chemoinformatics visualization package Scaffviz [56]. Scaffviz offers alternative visualization of model errors and is essentially an adapter between molplotly [79] and QSPRpred.…”

Section: Visualizationmentioning

confidence: 99%

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QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool

van den Maagdenberg,

Šícho,

Alencar Araripe

et al. 2024

Preprint

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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 integrate 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 Contribution QSPRpred 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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Section: Data Pre-processingmentioning

confidence: 99%

Section: Visualizationmentioning

confidence: 99%