Combinatorial Library Enumeration and Lead Hopping using Comparative Interaction Fingerprint Analysis and Classical 2D QSAR Methods for Seeking Novel GABA_A α₃ Modulators

Abstract

Selective modulators of GABA(A) alpha(3) (gamma amino butyric acid alpha(3)) receptor are known to alleviate the side effects associated with nonspecific modulators. A follow up study was undertaken on a series of functionally selective phthalazines with an ideological credo of identifying more potent isofunctional chemotypes. A bioisosteric database enumerated using the combichem approach endorsed mining in a lead-like chemical space. Primary screening of the massive library was undertaken using the "Miscreen" toolkit, which uses sophisticated bayesian statistics for calculating bioactivity score. The resulting subset, thus, obtained was mined using a novel proteo-chemometric method that integrates molecular docking and QSAR formalism termed CoIFA (comparative interaction fingerprint analysis). CoIFA encodes protein-ligand interaction terms as propensity values based on a statistical inference to construct categorical QSAR models that assist in decision making during virtual screening. In the absence of an experimentally resolved structure of GABA(A) alpha(3) receptor, standard comparative modeling techniques were employed to construct a homology model of GABA(A) alpha(3) receptor. A typical docking study was then carried out on the modeled structure, and the interaction fingerprints generated based on the docked binding mode were used to derive propensity values for the interacting atom pairs that served as pseudo-energy variables to generate a CoIFA model. The classification accuracy of the CoIFA model was validated using different metrics derived from a confusion matrix. Further predictive lead mining was carried out using a consensus two-dimensional QSAR approach, which offers a better predictive protocol compared to the arbitrary choice of a single QSAR model. The predictive ability of the generated model was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. Few analogs designed using the concept of bioisosterism were found to be promising and could be considered for synthesis and subsequent screening.

The synthesis and analgesic properties of N-(benzyl)-2-hydroxy-9-methyl-4-oxo-4H-pyrido[1,2-a]pyrimidine-3-carboxamides

“…The theoretical precondition for such modification was the methodology of bioisosteric replacements widely and effectively used by modern medical chemistry, involving the replacement of one group in the molecule close to it by the properties [11,12]. It should be remembered that the bioisosteric groups are groups that are the same not only in size or volume, but have similar physical and chemical properties, and therefore, reveal a similar pharmacological effect [13][14][15].…”

Deciphering the Structural Requirements of Nucleoside Bisubstrate Analogues for Inhibition of MbtA in Mycobacterium tuberculosis: A FB‐QSAR Study and Combinatorial Library Generation for Identifying Potential Hits

“…[50] The SAR report functionality of MOE was used to identify key R-group motifs [51,52] that were structurally transformed using the concept of bioisosterism. [36,53,54] Potential isosters for the marked Markush fragments were identified from a biosisosteric database containing fragments of synthetic tractability using the Brood software. [39] Four classes of isosters that match the query fragments in terms of shape, atom-type, electrostatics, structure, and graph were identified.…”

“…QuaSAR CombiGen module of MOE was then used to enumerate a virtual library of all possible products that could be combinatorially obtained from the set of fragments. [36] The targeted library containing 2,37,720 compounds, thus generated, can be screened en masse and yield better enrichment rates than either a random or diverse library. [55] Markush fragments considered for isosteric replacement are shown in Figure 3 and the core scaffolds used for clipping the fragments are shown in Figure 2.…”

“…Thus, the principle of analog design, widely exploited in medicinal chemistry, was used to create a targeted combinatorial library. [36] Computational methods are being increasingly used to assist the combinatorial library design, focusing, and virtual screening by introducing selection criteria such as molecular diversity, drug likeness, receptor binding analysis, and ADME properties of analogs. Computational approaches can thus significantly reduce the cost, time, and labor required to synthesize and screen large libraries, as well as enhance the success rate in lead compound generation.…”

Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds

“…However, Bayesian approach, on account of the frequency of various descriptors occurrence, is a robust classification methodology which can differentiate actives from inactives [14,16]. Many examples have demonstrated its application in structure-activity analysis and drug discovery [14,[17][18][19][20][21]. Besides, several studies have shown that Bayesian categorization demonstrates higher EF and accuracy than molecular docking in identification of actives from decoys [22].…”