Abstract
Predicting compounds with single- and multi-target activity and exploring origins of compound specificity and promiscuity is of high interest for chemical biology and drug discovery. We present a large-scale analysis of compound promiscuity including two major components. First, high-confidence datasets of compounds with multi- and corresponding single-target activity were extracted from biological screening data. Positive and negative assay results were taken into account and data completeness was ensured. Second, these datasets were investigated using diagnostic machine learning to systematically distinguish between compounds with multi- and single-target activity. Models built on the basis of chemical structure consistently produced meaningful predictions. These findings provided evidence for the presence of structural features differentiating promiscuous and non-promiscuous compounds. Machine learning under varying conditions using modified datasets revealed a strong influence of nearest neighbor relationship on the predictions. Many multi-target compounds were found to be more similar to other multi-target compounds than single-target compounds and vice versa, which resulted in consistently accurate predictions. The results of our study confirm the presence of structural relationships that differentiate promiscuous and non-promiscuous compounds.
Highlights
The term compound promiscuity is often used with opposing connotations including the presence of non-specific, artificial, or otherwise undesired binding effects on the one hand, and the ability to engage in specific interactions with multiple targets on the other [1]
Applying the lower threshold promiscuity degrees (PDs) ≥ 3 for multi-target compounds (MT-CPDs) led to the identification of 3468 and 11,793 MT- and corresponding ST-CPDs from biochemical assays and further increased the size of the dataset
From the top to the bottom, results are shown for all ST- and MT-CPDs (All CPDs), reduced data sets after random removal of 50% of the compounds (Random removal), and corresponding data sets after removal of 50% of nearest neighbor (NN) (NN removal)
Summary
The term compound promiscuity is often used with opposing connotations including the presence of non-specific, artificial, or otherwise undesired binding effects on the one hand, and the ability to engage in specific interactions with multiple targets on the other [1]. Insights into the molecular origins of promiscuity would aid in the design of small molecules with pre-defined multi-target activities [13,14,15,16,17]. Designing such ligands is of high interest for polypharmacology, and relevant for the concomitant use of chemical probes with defined dual-target activity and corresponding single-target activity, which enables the detection of synergistic functional effects. In addition to generating such hybrid compounds, scaffolds shared by compounds having different activity can be considered as templates for multi-target ligand design [18]
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