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Autori: Oprea, Tudor I.
Editorial: J. Comput.-Aided Mol. Des., 14(3), p.251-264, 2000.
The process of compd. selection and prioritization is crucial for both combinatorial chem. (CBC) and high throughput screening (HTS). Compd. libraries have to be screened for unwanted chem. structures, as well as for unwanted chem. properties. Property extrema can be eliminated by using property filters, in accordance with their actual distribution. Property distribution was examd. in the following compd. databases: MACCS-II Drug Data Report (MDDR), Current Patents Fast-alert, Comprehensive Medicinal Chem., Physician Desk Ref., New Chem. Entities, and the Available Chem. Directory (ACD). The ACDF and MDDRF subsets were created by removing reactive functionalities from the ACD and MDDR databases, resp. The ACDF subset was further filtered by keeping only mols. with a “drug-like” score below 0.8. The following properties were examd.: mol. wt. (MW), the calcd. octanol/water partition coeff. (CLOGP), the no. of rotatable (RTB) and rigid bonds (RGB), the no. of rings (RNG), and the no. of hydrogen bond donors (HDO) and acceptors (HAC). Of these, MW and CLOGP follow a Gaussian distribution, whereas all other descriptors have an asym. (truncated Gaussian) distribution. Four out of five compds. in ACDF and MDDRF pass the “rule of 5” test, a probability scheme that ests. oral absorption proposed by Lipinski et al. Because property distributions of HDO, HAC, MW and CLOGP (used in the “rule of 5” test) do not differ significantly between these datasets, the “rule of 5” does not distinguish “drugs” from “nondrugs”. Therefore, Pareto analyses were performed to examine skewed distributions in all compd. collections. Seventy percent of the “drug-like” compds. were found between the following limits: 0 <= HDO <= 2, 2 <= HAC <= 9, 2 <= RTB <= 8, and 1 <= RNG <= 4, resp. The no. of launched drugs in MDDR having 0 <= HDO <= 2 is 4.8 times higher than the no. of drugs having 3 <= HDO <= 5. Skewed distributions can be exploited to focus on the "drug-like space": 62.68% of ACDF ("nondrug-like") compds. have 0 <= RNG <= 2, and RGB <= 17, while 28.88% of ACDF compds. have 3 <= RNG <= 13, and 18 <= RGB <= 56. By contrast, 61.22% of MDDRF compds. have RNG =< 3, and RGB =< 18, and only 24.73% of MDDRF compds. have 0 <= RNG <= 2 rings, and RGB <= 17. The probability of identifying "drug-like" structures increases with mol. complexity.
Cuvinte cheie: Combinatorial chemistry, Computer application, Databases, Drug design, Drugs, Hydrogen bond, Molecular weight, Partition, QSAR, Structure-activity relationship