Abstract
The concept of drug-likeness has been widely applied in combinatorial chemistry as an approach to reduce attrition in drug discovery and development. Meanwhile, bicyclic privileged structures with versatile binding properties have emerged as ideal source of core scaffolds for the design and synthesis of combinatorial libraries. For the purpose of better assisting the design of bicyclic privileged structure-based combinatorial libraries, we conducted an integrated drug-likeness study on compounds of these scaffolds. Distributions of physicochemical properties (PCPs) were analyzed and in silico prediction models were built. Our results showed that there exist much difference between the drug-like ranges (DLRs) of bicyclic privileged structures and that of others, which have significant impact on compound selection. The DLRs for bicyclic privileged structures were defined as 260 ≤ MW ≤ 524; 0.9 ≤ ALogP ≤ 5.4; 2 ≤ Hacc ≤ 8; Hdon ≤ 3; 21.0 ≤ PSA ≤ 128.6; 6.3 ≤ FPSA ≤ 34.2; 1 ≤ RotB ≤ 10; 2 ≤ Nr ≤ 5; 1 ≤ Nc ≤ 7; SA ≤ 4. Two accurate and easy to understand in silico prediction models, Caco-2 permeability model and metabolic stability classification model, had been built to guide drug candidate optimization. In these models, hydrogen-bond donor and rotatable bond showed major impact on the permeability of compounds, while lipophilicity, flexibility, degree of branching and the existence of some functional groups determined the fate of a drug in metabolic process. Suggestions on structural modification toward higher permeability and metabolic stability were given according to the in silico models.
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