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,
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 structurebased
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 druglike
ranges(DLRs)ofbicyclic privilegedstructures andthat 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 silicomodels.