ss-master: added logs

paper.md

@@ -227,14 +227,15 @@ extend to. If we look at one trend of the data of four leaf nodes: BRAF Kinases
 that are toxic to us. We expected `CGenFF` to perform on anthing drug-like and not so much for chemical manufacturing. 
 
 In `Figure 6`, we can see that `CGenFF` is performing well on parameter assignment for BRAF Kinase Inhibitors owed to its initial training set of "drug-like" molecules. For mother nature's elected scaffolds for small molecules which also mimics the definition of "drug-like".
-For covalent inhibitor warheads, non-traditional "drug-like" molecules given their purpose
-is to bind into the receptor which is only a newly established recent ascent [Gehringer:2019-6]. The penalty score is more dispersed
+If we extend our look into narcotics that are banned by the drug enforcement agency (DEA) listed as a series of schedules [21CFRPart1] but still inherit drug name, `CGenFF` still performs well on what drugs are popular in the black market. For something no traditional binding we can look chemical covalent inhibitor warheads lists, which are "drug-like" molecules given their purpose
+is to bind into the receptor with a physical bond which is only a newly established recent ascent [Gehringer:2019-6]. The penalty score is more dispersed
 because these are recent advancements in science that could be useful to the drug-like community. It still behaves well with 
 some outlier compounds that we can focus on parametirizing. And lastly, perfluoroalkyls are used in chemical manufacturing 
 of everyday goods [Pelch:2019-9]. This is not what `CGenFF` was intended for but still assigns parameters that do not extend
 too far from the existing atom-types built into the forcefield. We can arguably say, that if we add one perfluoroalkyl to
 `CGenFF` it will help reduce penalties for the rest of the dataset as well be useful to the chemical hazard community.
 
+
 # Statement of Purpose
 
 Chemical lists are private information that is buried within corporations protected under intellectual property/non-disclosure agreements or academic papers imprisoned by the paywall publication system of their own making. Neither academia nor industry is allowing real sciencee to develop. Instead of sharing information we rely on complicated hard-coded algorithms making arbitrary decisions without actual scientific backing. `Global-Chem` has a lot potential purposes (teaching, inferences, cheminformatics) but our main perogative is to create a free record collection. If we plant the seed now then, hopefully, our tree will grow. As it grows, the answers we seek will become more clear and reasoned with some level of scientific thought. Together, we can build the biggest network regardless of where you are from and who you are.