What question did this study set out to answer?

To establish standardized community guidelines and open-source tools for curating, preparing, and statistically evaluating protein-ligand binding affinity benchmarks.

August 8, 2022Living Journal of Computational Molecular ScienceOpen Access

Best Practices for Constructing, Preparing, and Evaluating Protein-Ligand Binding Affinity Benchmarks Article v1.0

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Authors

DHDavid F. HahnJanssen (Belgium)CBChristopher I. BaylyOpen Source Science Project MBMelissa L. BobyMemorial Sloan Kettering Cancer Center

Discussion

Member takes

Implication

Methodological framework demonstrates standardized benchmarking protocols in computational affinity prediction, highlighting best practices for reliable drug discovery evaluation.

Key Points

To establish standardized community guidelines and open-source tools for curating, preparing, and statistically evaluating protein-ligand binding affinity benchmarks.
Formulated standardized guidelines across experimental data curation, computational input preparation, and statistical evaluation of affinity predictions.
Developed 'protein-ligand-benchmark', a curated, versioned, open-access dataset designed to test free energy and machine learning models.
Engineered 'arsenic', an open-source software toolkit dedicated to executing standardized statistical assessments of prediction performance.
Identified critical failure modes in existing benchmark reports, notably substandard data curation, inadequate statistical power, and biased analytical comparisons.
Established a reproducible benchmark framework that provides realistic performance expectations for molecular simulation force fields and machine learning algorithms.

Cite This Study

Hahn et al. (2022) studied this question.

synapsesocial.com/papers/6a0baf13faed69294fd0b078 https://doi.org/10.33011/livecoms.4.1.1497

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August 8, 2022Living Journal of Computational Molecular ScienceOpen Access

Best Practices for Constructing, Preparing, and Evaluating Protein-Ligand Binding Affinity Benchmarks Article v1.0

View Full Paper

Ask AI

Bookmark

Authors

DHDavid F. HahnJanssen (Belgium)CBChristopher I. BaylyOpen Source Science Project MBMelissa L. BobyMemorial Sloan Kettering Cancer Center

Discussion

Member takes

Implication

Methodological framework demonstrates standardized benchmarking protocols in computational affinity prediction, highlighting best practices for reliable drug discovery evaluation.

Key Points

To establish standardized community guidelines and open-source tools for curating, preparing, and statistically evaluating protein-ligand binding affinity benchmarks.
Formulated standardized guidelines across experimental data curation, computational input preparation, and statistical evaluation of affinity predictions.
Developed 'protein-ligand-benchmark', a curated, versioned, open-access dataset designed to test free energy and machine learning models.
Engineered 'arsenic', an open-source software toolkit dedicated to executing standardized statistical assessments of prediction performance.
Identified critical failure modes in existing benchmark reports, notably substandard data curation, inadequate statistical power, and biased analytical comparisons.
Established a reproducible benchmark framework that provides realistic performance expectations for molecular simulation force fields and machine learning algorithms.

Cite This Study

Hahn et al. (2022) studied this question.

synapsesocial.com/papers/6a0baf13faed69294fd0b078 https://doi.org/10.33011/livecoms.4.1.1497

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