similarity-and-connectivity.md

+# Similarity and connectivity
+
+:construction: This page is under construction.
+
+In the context of the CC, "connectivity" is a generalization of "similarity". That is, it is a more flexible means to compare entities. The notion of connectivity is of special interest to *unsupervised* drug discovery since it enables mapping of external biological data to the chemical space.
+
+In the [CC pipeline](production phase), connectivity happens at the pre-processing step. The pre-processing step has [two phases](datasets):
+
+1. XX
+2. XX
+
+More precisely, connectivity starts with **standard input files** and finishes with a **signature type 0**.
+
+In some datasets, this procedure may be of considerable complexity and we need to conceive workflows that can be wrapped into a `predict()` method. For other datasets, the procedure will be almost trivial.
+
+Another important matter here is the distance. The CC works with *common* distance metrics, such as the `cosine` or `euclidean` distances. Sometimes, connectivity may require other types of metrics (e.g. GSEA-like, overlap, etc.). We might consider learning siamese networks that transform original distances to the more standard ones. This is an unexplored avenue, though.
+
+## Chemistry
+
+
+## Targets
+
+
+## Networks
+
+
+## Cells
+
+
+## Diseases
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