Amines pharmacophore points

Amines are eonsidered pharmacophore points but not azoles or diazines. 

Intracyclic amines in the same ring are fused to one pharmacophore point. 

The requirement of two distinct pharmacophore points neglects at least one very important class of drugs biogenic amine-containing CNS drugs. Therefore, a second pharmacophore filter has been designed that requires only one pharmacophore point in small molecules of the type amine, amidine, guanidine, or carboxylic acid (PF2). 

The basic set of Pharmacophore Points includes amine, amide, alcohol, ketone, sulfone, sulfonamide, carboxylic acid, carbamate, guaifidine, amidine, urea, and ester. 

Intracycfic amines that occur in the same ring (e.g. piperazine) are counted as one Pharmacophore Point. 

Primary, secondary and tertiary amines are considered Pharmacophore Points, but not pyrrole, indole, thiazole, isoxazole, other azoles and diazines. 

In a modified version of the pharmacophore point filter, a score equal to 1 is allowed to qualify a compound as drug-like if the pharmacophore point present in the compound is of type carboxylic acid, amine, guanidine, or amidine. This modified filter was defined to correctly classify the small active drugs belonging to the central nervous system (CNS) class. 

Retrosynthetic analysis of the oxazolidinone pharmacophore 1 shown in Fig. 3 revealed that the oxazolidinone aminomethyl group could serve as an attachment site to the solid support during the elaboration of a suitably substituted scaffold. We envisioned amine scaffold 2 could be readily coupled to a resin-bound aldehyde. Attachment of two points of diversity via derivatization of the solid-supported secondary amine 3 with a variety... 

The DIVSEL program was developed by Pickett et al. for combinatorial reagent selection using three-point pharmacophores as the descriptor for similarity calculations [2], The algorithm starts by selecting the compound most dissimilar to the others in the set and then iteratively selects compounds most dissimilar to those already selected. DIVSEL was used to select a set of carboxylic acids from a collection of 1100 monocarboxylic acids for an amide library, based on the pharmacophoric diversity of the products. Eleven diverse amines were selected based on pharmacophoric diversity. A virtual library of 12100 amides was constructed from the 11 amines and 1100 carboxylic acids. The DIVSEL program used the pharmacophore fingerprints for the product virtual library to select a diverse set of the carboxylic acids. The products of 90 acids with the 11 amines selected with DIVSEL covered 85% of the three-point pharmacophores represented by the entire 12100 compound virtual library. 

Several product-based approaches to library design that do not require full enumeration have been developed. Pickett et al. have described the design of a diverse amide library where diversity is measured in product space. The DIVSEL program is a DBCS method where dissimilarity is measured in three-point pharmacophore space [83]. Initially, 11 amines were selected based on maximum pharmacophore diversity. Then a total of 1100 carboxylic acids were identified following substructure searching. A set of 1100 pharmacophores keys was generated, where each key corresponds to one acid combined with the 11 amines. DIVSEL was used to select 100 acids based on the diversity of the products. The final library was found to cover 85% of the pharmacophores represented by the entire 12,100 virtual libraries. 

Beno and Mason [19] describe a product-based method based on simulated annealing that simultaneously optimizes four-point pharmacophore coverage and BCUT diversity. The virtual library is preenumerated however, the four-point pharmacophores are calculated on-the-fly, that is, during the optimization itself. The approach was used to select 20 carboxylic acids and 20 amines from a virtual library of 86,140 amines (292 acids and 295 amines). The library was optimized on pharmacophore coverage simultaneously with diversity in BCUT space. They found a 20-23% increase in BCUT cell coverage and a 1.8- to 2.6-fold increase in the number of pharmacophores covered compared with randomly selected reagents. 

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