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.

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.

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.