similarity-and-connectivity.md

@@ -68,7 +68,7 @@ Obviously, it is mandatory that the *vocabularies* used in the production phase
 
 From Signature Type 0 onwards, the CC only deals with two distance metrics: the cosine distance and the Euclidean distance. These are well-accepted metrics that capture two different properties: the direction and the absolute distance, respectively. 
 
-It may happen that some datasets require more advanced metrics, though. In this case, we recommend applying any required **transformation** of the data in the pre-processing, so as Signatures Type 0 are natively comparable using cosine/Euclidean distances. This can be achieved by metric learning algorithms. For example, one incorporate a Siamese network in the pre-processing:
+It may happen that some datasets require more advanced metrics, though. In this case, we recommend applying any required **transformation** of the data in the pre-processing, so as Signatures Type 0 are natively comparable using cosine/Euclidean distances. This can be achieved by metric learning algorithms. For example, one can incorporate a Siamese network in the pre-processing:
 
 ![connectivity_examples-02](/uploads/4205269881f764f5e74af564838ebc10/connectivity_examples-02.png)
 
@@ -76,6 +76,8 @@ It may happen that some datasets require more advanced metrics, though. In this
 
 The *mapping* (prediction) for new molecules/entities can be entered at one or multiple steps of the predict pipeline. The corresponding argument is `entry_point`.
 
+Please note that, in this case, the key that is kept in the Signature Type 0 is exactly the one provided by the user.
+
 ### `A` Chemistry
 
 #### `A1.001` 2D fingerprints