Evaluation of qsar equations for virtual screening

Jacob Spiegel, Hanoch Senderowitz

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure‐based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties and for designing new compounds with favorable properties. Due to the large number of calculate‐able descriptors and consequently, the much larger number of descriptors combinations, the derivation of QSAR models could be treated as an optimization problem. For continuous responses, metrics which are typically being optimized in this process are related to model performances on the training set, for example, R2 and Q2 2 cv. Similar metrics, calculated on an external set of data (e.g., QF1/F2/F3), are used to evaluate the performances of the final models. A common theme of these metrics is that they are context ‐” ignorant”. In this work we propose that QSAR models should be evaluated based on their intended usage. More specifically, we argue that QSAR models developed for Virtual Screening (VS) should be derived and evaluated using a virtual screening‐aware metric, e.g., an enrichment‐based metric. To demonstrate this point, we have developed 21 Multiple Linear Regression (MLR) models for seven targets (three models per target), evaluated them first on validation sets and subsequently tested their performances on two additional test sets constructed to mimic small‐scale virtual screening campaigns. As expected, we found no correlation between model performances evaluated by “classical” metrics, e.g., R2 2 and QF1/F2/F3 and the number of active compounds picked by the models from within a pool of random compounds. In particular, in some cases models with favorable R2 2 and/or QF1/F2/F3 values were unable to pick a single active compound from within the pool whereas in other cases, models with poor R2 2 and/or QF1/F2/F3 values performed well in the context of virtual screening. We also found no significant correlation between the number of active compounds correctly identified by the models in the training, validation and test sets. Next, we have developed a new algorithm for the derivation of MLR models by optimizing an enrichment‐based metric and tested its performances on the same datasets. We found that the best models derived in this manner showed, in most cases, much more consistent results across the training, validation and test sets and outperformed the corresponding MLR models in most virtual screening tests. Finally, we demonstrated that when tested as binary classifiers, models derived for the same targets by the new algorithm outperformed Random Forest (RF) and Support Vector Machine (SVM)‐based models across training/validation/test sets, in most cases. We attribute the better performances of the Enrichment Optimizer Algorithm (EOA) models in VS to better handling of inactive random compounds. Optimizing an enrichment‐based metric is therefore a promising strategy for the derivation of QSAR models for classification and virtual screening.

Original languageEnglish
Article number7828
Pages (from-to)1-20
Number of pages20
JournalInternational Journal of Molecular Sciences
Issue number21
StatePublished - 22 Oct 2020

Bibliographical note

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.


The authors are grateful to Alexander Tropsha from UNC for sharing the hERG dataset and to Malkeet Singh Bahia for help with processing the M2, H1 and 5HT2C datasets.

FundersFunder number
University of North Carolina


    • Enrichment‐based optimization
    • Multiple linear regression (MLR), random forest (RF), support vector machine (SVM), enrichment optimizer algorithm (EOA)
    • QSAR equations
    • Quantitative Structure Activity Relationship (QSAR) models
    • Virtual screening (VS)


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