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Physicochemical QSAR analysis of hERG inhibition revisited: towards a quantitative potency prediction

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Abstract

In an earlier study (Didziapetris R & Lanevskij K (2016). J Comput Aided Mol Des. 30:1175–1188) we collected a database of publicly available hERG inhibition data for almost 6700 drug-like molecules and built a probabilistic Gradient Boosting classifier with a minimal set of physicochemical descriptors (log P, pKa, molecular size and topology parameters). This approach favored interpretability over statistical performance but still achieved an overall classification accuracy of 75%. In the current follow-up work we expanded the database (provided in Supplementary Information) to almost 9400 molecules and performed temporal validation of the model on a set of novel chemicals from recently published lead optimization projects. Validation results showed almost no performance degradation compared to the original study. Additionally, we rebuilt the model using AFT (Accelerated Failure Time) learning objective in XGBoost, which accepts both quantitative and censored data often reported in protein inhibition studies. The new model achieved a similar level of accuracy of discerning hERG blockers from non-blockers at 10 µM threshold, which can be conceived as close to the performance ceiling for methods aiming to describe only non-specific ligand interactions with hERG. Yet, this model outputs quantitative potency values (IC50) and is not tied to a particular classification cut-off. pIC50 from patch-clamp measurements can be predicted with R2 ≈ 0.4 and MAE < 0.5, which enables ligand ranking according to their expected potency levels. The employed approach can be valuable for quantitative modeling of various ADME and drug safety endpoints with a high prevalence of censored data.

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Data availability

All datasets collected and analyzed during this study are available in supplementary information.

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Authors and Affiliations

  1. VšĮ “Aukštieji algoritmai”, A.Mickevičiaus 29, LT-08117, Vilnius, Lithuania

    Kiril Lanevskij, Remigijus Didziapetris & Andrius Sazonovas

  2. ACD/Labs, Inc, 8 King Street East, Suite 107, M5C 1B5, Toronto, ON, Canada

    Kiril Lanevskij, Remigijus Didziapetris & Andrius Sazonovas

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  1. Kiril Lanevskij
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Contributions

All authors contributed to the study conception and design. RD collected and curated literature data. KL performed statistical modeling and visualization. All authors were involved in the analysis and interpretation of the results. KL wrote the first draft of the manuscript. RD and AS reviewed the manuscript. All authors read and approved the final manuscript.

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Correspondence to Kiril Lanevskij.

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Lanevskij, K., Didziapetris, R. & Sazonovas, A. Physicochemical QSAR analysis of hERG inhibition revisited: towards a quantitative potency prediction. J Comput Aided Mol Des 36, 837–849 (2022). https://doi.org/10.1007/s10822-022-00483-0

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