Journal of Computer-Aided Molecular Design

, Volume 14, Issue 3, pp 251–264 | Cite as

Property distribution of drug-related chemical databases*

  • Tudor I. Oprea
Article

Abstract

The process of compound selection and prioritization is crucial for both combinatorial chemistry (CBC) and high throughput screening (HTS). Compound libraries have to be screened for unwanted chemical structures, as well as for unwanted chemical properties. Property extrema can be eliminated by using property filters, in accordance with their actual distribution. Property distribution was examined in the following compound databases: MACCS-II Drug Data Report (MDDR), Current Patents Fast-alert, Comprehensive Medicinal Chemistry, Physician Desk Reference, New Chemical Entities, and the Available Chemical Directory (ACD). The ACDF and MDDRF subsets were created by removing reactive functionalities from the ACD and MDDR databases, respectively. The ACDF subset was further filtered by keeping only molecules with a `drug-like' score [Ajay et al., J. Med. Chem., 41 (1998) 3314; Sadowski and Kubinyi, J. Med. Chem., 41 (1998) 3325] below 0.8. The following properties were examined: molecular weight (MW), the calculated octanol/water partition coefficient (CLOGP), the number of rotatable (RTB) and rigid bonds (RGB), the number of rings (RNG), and the number of hydrogen bond donors (HDO) and acceptors (HAC). Of these, MW and CLOGP follow a Gaussian distribution, whereas all other descriptors have an asymmetric (truncated Gaussian) distribution. Four out of five compounds in ACDF and MDDRF pass the `rule of 5' test, a probability scheme that estimates oral absorption proposed by Lipinski et al. [Adv. Drug Deliv. Rev., 23 (1997) 3]. 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 compound collections. Seventy percent of the `drug-like' compounds were found between the following limits: 0 ≤ HDO ≤ 2, 2 ≤ HAC ≤ 9, 2 ≤ RTB ≤ 8, and 1 ≤ RNG ≤ 4, respectively. The number of launched drugs in MDDR having 0 ≤ HDO ≤ 2 is 4.8 times higher than the number of drugs having 3 ≤ HDO ≤ 5. Skewed distributions can be exploited to focus on the `drug-like space': 62.68% of ACDF (`nondrug-like') compounds have 0 ≤ RNG ≤ 2, and RGB ≤ 17, while 28.88% of ACDF compounds have 3 ≤ RNG ≤ 13, and 18 ≤ RGB ≤ 56. By contrast, 61.22% of MDDRF compounds have RNG ≥ 3, and RGB ≥ 18, and only 24.73% of MDDRF compounds have 0 ≤ RNG ≤ 2 rings, and RGB ≤ 17. The probability of identifying `drug-like' structures increases with molecular complexity.

combinatorial chemistry computer chemistry database filtering drug-likeness drug research hydrogen bonds property distribution `rule of 5' test 

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Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Tudor I. Oprea
    • 1
  1. 1.Medicinal Chemistry, Astra Hässle ABMölndalSweden

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