Interpreting linear support vector machine models with heat map molecule coloring

<p>Abstract</p> <p>Background</p> <p>Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity.</p> <p>Results</p> <p>We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor.</p> <p>Conclusions</p> <p>In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor.</p

Turner syndrome and sexual differentiation of the brain: implications for understanding male-biased neurodevelopmental disorders

Turner syndrome (TS) is one of the most common sex chromosome abnormalities. Affected individuals often show a unique pattern of cognitive strengths and weaknesses and are at increased risk for a number of other neurodevelopmental conditions, many of which are more common in typical males than typical females (e.g., autism and attention-deficit hyperactivity disorder). This phenotype may reflect gonadal steroid deficiency, haploinsufficiency of X chromosome genes, failure to express parentally imprinted genes, and the uncovering of X chromosome mutations. Understanding the contribution of these different mechanisms to outcome has the potential to improve clinical care for individuals with TS and to better our understanding of the differential vulnerability to and expression of neurodevelopmental disorders in males and females. In this paper, we review what is currently known about cognition and brain development in individuals with TS, discuss underlying mechanisms and their relevance to understanding male-biased neurodevelopmental conditions, and suggest directions for future research