Abstract
A computational procedure to search for selective ligands for structurally related protein targets was developed and verified for serotonergic 5-HT7/5-HT1A receptor ligands. Starting from a set of compounds with annotated activity at both targets (grouped into four classes according to their activity: selective toward each target, not-selective and not-selective but active) and with an additional set of decoys (prepared using DUD methodology), the SVM (Support Vector Machines) models were constructed using a selective subset as positive examples and four remaining classes as negative training examples. Based on these four component models, the consensus classifier was then constructed using a data fusion approach. The combination of two approaches of data representation (molecular fingerprints vs. structural interaction fingerprints), different training set sizes and selection of the best SVM component models for consensus model generation, were evaluated to determine the optimal settings for the developed algorithm. The results showed that consensus models with molecular fingerprints, a larger training set and the selection of component models based on MCC maximization provided the best predictive performance.
Highlights
The identification of ligands that display a high affinity for one protein target and that are significantly less active for another or a group of closely related members of a given family is of high relevance for modern drug discovery
It should be emphasized that in the present analysis we focused on comparing the performance of the designed algorithm in different settings in terms of its ability to distinguish Selective from not-selective, inactive, decoy and multimodal compounds for the virtual screening of molecular databases
As demonstrated in the present analysis, machine learning classification models trained on a set of ligands with different selectivity and activity profiles can provide a consensus model, the performance of which is significantly better than the component models
Summary
The identification of ligands that display a high affinity for one protein target and that are significantly less active for another or a group of closely related members of a given family is of high relevance for modern drug discovery. Apart from using selective ligands as leads in drug design workflows, they can be applied as molecular probes for studying, e.g., cellular functions [1]. Because the validation of compound selectivity requires significant experimental efforts and financial resources, fast and accurate computational methods to predict ligand selectivity are highly desirable. Diverse computational ligand- and/or structure-based approaches to explain the molecular mechanism of selectivity and/or to predict compound selectivity have been. Other studies have described QSAR modeling to predict the ligand selectivity for serotonin 5-HT1E/5-HT1F[5] or dopamine D2/D3 receptors [6] and for a panel of 45 different kinases [7]. Other investigations used machine learning (ML) methods to construct selectivity prediction models, e.g., ML based on neural networks to generate structure-selectivity relationship models [8], the binary classification SVM (Support Vector Machines) algorithm to solve multiclass predictions and compound ranking to distinguish between selective, active but non-selective, and inactive compounds [9], and the LiCABEDS (Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps) algorithm to model cannabinoid CB1/CB2 selectivity [10]
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