Evaluation of qsar equations for virtual screening

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., Q_F1/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 Q_F1/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 Q_F1/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 Q_F1/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.