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The Drug-Target Interaction Heatmap

The Drug-Target Interaction Heatmap

A heatmap is a two-dimensional data visualization approach that displays the magnitude of a phenomenon as color. The color shift might be via hue or intensity, giving the reader clear visual indications about how the occurrence is clustered or evolves over space. Heatmaps are classified into two types: cluster heatmaps and spatial heatmaps. The sorting of rows and columns is intentional and somewhat arbitrary in a clustered heatmap, and the magnitudes are laid out into a matrix of fixed cell size whose rows and columns are discrete phenomena and categories, to suggest clusters or portray them as discovered via statistical analysis. The cell size is arbitrary, but it must be large enough to be seen. The position of a magnitude on a spatial heatmap, on the other hand, is determined by its location in that space, and there is no concept of cells; the phenomena are assumed to change continuously.

Data scientists and data analysts examine and determine essential links and characteristics among different points in a dataset, as well as aspects of those data points when working with small and large datasets. Heatmaps depict these data points and their interactions in a high-dimensional context without becoming excessively compressed and visually unpleasant. In data analysis, heatmaps enable specific variables of rows and/or columns to be plotted on the axes.

The drug-target interaction heatmap facilitates decision-making about the potential off-target activity of drug candidates early in the new drug development and drug repurposing workflows. In terms of important factors, a heatmap provides a clear picture of the interactions between drugs and their targets. This allows for the quick identification of the most important interactions.

GOSTAR presents these findings in a visually intuitive manner, allowing end-users to easily interpret the data and draw conclusions.

References

  1. Heat map. In: Google Arts and Culture. https://artsandculture.google.com/entity/heat-map/m09yl47?hl=en Accessed 21 April 2022.
  2. Exploratory Data Analysis. In: IBM Cloud Learn Hub. https://www.ibm.com/cloud/learn/exploratory-data-analysis Accessed 21 April 2022.
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Matched Molecular Pair Analysis

Matched Molecular Pair Analysis

The complexity in molecular design is selecting what to do next based on existing data, medicinal chemistry knowledge, experience, and intuition. In small compound sets, a skilled chemist can discern trends and correlations by eye. As the number of molecules increases, more methodical procedures are required.

The Matched Molecular Pair (MMP) analysis, which compares closely related chemical structures pairwise across a big dataset, is one method in the medicinal chemist’s toolbox for accomplishing this. Since the structures of the two molecules in question differ very slightly, any change in a physical or biological feature between the matched molecular pair can be more easily interpreted.

In 2004, Kenny and Sadowski coined the term Molecular Matched Pair (MMP) for a subset of QSAR; it is now a widely used concept in drug design processes [1]. Matched molecular pairs differ only in small single-point alterations, which are referred to as chemical transformations. As the structural difference between the two molecules is minimal, any differences in physical properties or observed biological effects can simply be linked to it. In 2010, Hussain and Rea published an approach to find matched molecular pairs and relate them to the distribution of value differences for each transformation, and it has since become a popular tool for analyzing huge chemistry datasets.

MMP is typically used to describe a pair of compounds that differ structurally at a single site because of a well-defined transformation accompanied by a change in a property value. To rationalize observed structure-property relationships (SPR) and compound optimization, the relationship between structural and property change is used. Aside from assisting in hypothesis creation and testing, MMP can also be used to find outliers, such as a pair of compounds that have a sudden change in a property, known as an activity cliff. These compounds are typically the most intriguing to investigate in the development of compounds aimed at increasing the property that exhibits this change.

GOSTAR provides tools for determining the matched molecular pairs and analyzing activity landscapes across compound datasets.

References

  1. Kenny P.W., Sadowski J. Structure modification in chemical databases. In: Oprea T., editor. Cheminformatics in drug discovery. Wiley-VCH Weinheim; Germany, 2004, 271.
  2. Hussain J, Rea C. Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model. 2010, 50(3), 339-348.
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Interactive Property Space Exploration

Interactive Property Space Exploration

Lipophilicity plays a significant role in small molecule drug design and discovery. A partition coefficient, logP, can be used to describe the lipophilicity of an organic compound. It is expressed as the ratio of the unionized compound’s concentration in the organic and aqueous phases at equilibrium. The distribution of species in compounds containing ionizable groups is influenced by pH and the lipophilicity of a molecule is affected by its ionization state. As a result, the distribution coefficient (logD) of a compound is defined, which considers the dissociation of weak acids and bases. In aqueous conditions, highly lipophilic substances are often less soluble. Lipophilic compounds, on the other hand, may have good solubility in oils and lipids, making them good candidates for lipid-based formulations.

Lipophilicity influences potency, selectivity, permeability, absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. High lipophilicity, with logP greater than five, is associated with limited solubility, increased clearance, and poor oral absorption. Furthermore, highly lipophilic drugs have a predisposition for interacting with hydrophobic targets other than the primary target, thereby enhancing promiscuity and toxicity. Low lipophilicity can reduce permeability and potency, resulting in lower bioavailability and overall efficacy. Compounds with logP greater than one or less than four are thought to have better physicochemical and ADME properties for oral drugs.

Lipophilicity is often regarded as a key indicator of potential promiscuity, with many property–promiscuity studies indicating that drug promiscuity rises with the increase in lipophilicity. This tendency is concerning since increasing a molecule’s lipophilicity can improve its efficacy at the primary target; however, this can be counterbalanced by an increase in off-target promiscuity [2]. Lipophilicity is a key element in determining a drug’s affinity for protein targets and in modulating ADMET characteristics. As a result, the combination of high target potency and high lipophilicity may increase the likelihood of ADMET-related attrition. 

Therefore, medicinal chemistry optimization needs to be balanced and multidimensional. GOSTAR empowers medicinal chemists to efficiently explore the property space against a variety of bioactivity endpoints.

References

  1. Gao Y, Gesenberg C, Zheng W. Oral Formulations for Preclinical Studies: Principle, Design, and Development Considerations, Developing Solid Oral Dosage Forms (Second Edition), Academic Press. 2017, 455-495.
  2. Armstrong D, Li S, Frieauff W, Martus H.J, Reilly J, Mikhailov D, Whitebread S, Urban L. Predictive Toxicology: Latest Scientific Developments and Their Application in Safety Assessment, Comprehensive Medicinal Chemistry III, Elsevier. 2017, 94-115.