PREFER: A New Predictive Modeling Framework for Molecular Discovery

Lanini, Jessica; Santarossa, Gianluca; Sirockin, Finton; Lewis, Richard A.; Fechner, Nikolas; Misztela, Hubert; Lewis, Sarah; Maziarz, Krzysztof; Stanley, Megan; Segler, Marwin; Stiefl, Nikolaus; Schneider, Nadine

doi:10.1021/acs.jcim.3c00523

Cited by 8 publications

(2 citation statements)

References 26 publications

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“…However, one limitation of ZairaChem is that it currently only supports classification models and also does not enable model serialization with preparation steps included. This is addressed in a recently published package, PREFER [49], which wraps trained models fully, including data preprocessing. It implements a pipeline based on AutoSklearn [50], covering steps such as data preparation, model selection, and model evaluation.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…Despite this, only four sets of algorithms are available, a restricted number of four descriptors are available (the MOE descriptors also require a license), and no GUI is provided. PREdictive modeling FramEwoRk (PREFER) was recently proposed by Lanini, Santarossa, Sirockin, Lewis, Fechner, Misztela, Lewis, Maziarz, Stanley, Segler et al In this package, popular libraries are used for hyperparameter optimization, with the authors stating that the most important factor is the ability to customize the framework. AutoSklearn is supported by an active community, but the package relies on notebooks and requires a detailed four-step installation process.…”

Section: Introductionmentioning

confidence: 99%

QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design

Mervin,

Voronov,

Kabeshov

et al. 2024

J. Chem. Inf. Model.

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Machine-learning (ML) and deep-learning (DL) approaches to predict the molecular properties of small molecules are increasingly deployed within the design−make−test−analyze (DMTA) drug design cycle to predict molecular properties of interest. Despite this uptake, there are only a few automated packages to aid their development and deployment that also support uncertainty estimation, model explainability, and other key aspects of model usage. This represents a key unmet need within the field, and the large number of molecular representations and algorithms (and associated parameters) means it is nontrivial to robustly optimize, evaluate, reproduce, and deploy models. Here, we present QSARtuna, a molecule property prediction modeling pipeline, written in Python and utilizing the Optuna, Scikit-learn, RDKit, and ChemProp packages, which enables the efficient and automated comparison between molecular representations and machine learning models. The platform was developed by considering the increasingly important aspect of model uncertainty quantification and explainability by design. We provide details for our framework and provide illustrative examples to demonstrate the capability of the software when applied to simple molecular property, reaction/reactivity prediction, and DNA encoded library enrichment classification. We hope that the release of QSARtuna will further spur innovation in automatic ML modeling and provide a platform for education of best practices in molecular property modeling. The code for the QSARtuna framework is made freely available via GitHub.

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ROBERT: Bridging the Gap Between Machine Learning and Chemistry

Dalmau,

Alegre‐Requena

2024

WIREs Comput Mol Sci

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Beyond addressing technological demands, the integration of machine learning (ML) into human societies has also promoted sustainability through the adoption of digitalized protocols. Despite these advantages and the abundance of available toolkits, a substantial implementation gap is preventing the widespread incorporation of ML protocols into the computational and experimental chemistry communities. In this work, we introduce ROBERT, a software carefully crafted to make ML more accessible to chemists of all programming skill levels, while achieving results comparable to those of field experts. We conducted benchmarking using six recent ML studies in chemistry containing from 18 to 4149 entries. Furthermore, we demonstrated the program's ability to initiate workflows directly from SMILES strings, which simplifies the generation of ML predictors for common chemistry problems. To assess ROBERT's practicality in real‐life scenarios, we employed it to discover new luminescent Pd complexes with a modest dataset of 23 points, a frequently encountered scenario in experimental studies.

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PREFER: A New Predictive Modeling Framework for Molecular Discovery

Cited by 8 publications

References 26 publications

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

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

QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design

ROBERT: Bridging the Gap Between Machine Learning and Chemistry

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