Literature DB >> 17333482

Lead-like, drug-like or "Pub-like": how different are they?

Tudor I Oprea1, Tharun Kumar Allu, Dan C Fara, Ramona F Rad, Lili Ostopovici, Cristian G Bologa.   

Abstract

Academic and industrial research continues to be focused on discovering new classes of compounds based on HTS. Post-HTS analyses need to prioritize compounds that are progressed to chemical probe or lead status. We report trends in probe, lead and drug discovery by examining the following categories of compounds: 385 leads and the 541 drugs that emerged from them; "active" (152) and "inactive" (1488) compounds from the Molecular Libraries Initiative Small Molecule Repository (MLSMR) tested by HTS; "active" (46) and "inactive" (72) compounds from Nature Chemical Biology (NCB) tested by HTS; compounds in the drug development phase (I, II, III and launched), as indexed in MDDR; and medicinal chemistry compounds from WOMBAT, separated into high-activity (5,784 compounds with nanomolar activity or better) and low-activity (30,690 with micromolar activity or less). We examined Molecular weight (MW), molecular complexity, flexibility, the number of hydrogen bond donors and acceptors, LogP-the octanol/water partition coefficient estimated by ClogP and ALOGPS), LogSw (intrinsic water solubility, estimated by ALOGPS) and the number of Rule of five (Ro5) criteria violations. Based on the 50% and 90% distribution moments of the above properties, there were no significant difference between leads of known drugs and "actives" from MLSMR or NCB (chemical probes). "Inactives" from NCB and MLSMR were also found to exhibit similar properties. From these combined sets, we conclude that "Actives" (569 compounds) are less complex, less flexible, and more soluble than drugs (1,651 drugs), and significantly smaller, less complex, less hydrophobic and more soluble than the 5,784 high-activity WOMBAT compounds. These trends indicate that chemical probes are similar to leads with respect to some properties, e.g., complexity, solubility, and hydrophobicity.

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Year:  2007        PMID: 17333482      PMCID: PMC2807375          DOI: 10.1007/s10822-007-9105-3

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  17 in total

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4.  Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices.

Authors:  I V Tetko; V Y Tanchuk; A E Villa
Journal:  J Chem Inf Comput Sci       Date:  2001 Sep-Oct

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7.  Application of ALOGPS to predict 1-octanol/water distribution coefficients, logP, and logD, of AstraZeneca in-house database.

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Journal:  J Comput Aided Mol Des       Date:  2005-06       Impact factor: 3.686

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  21 in total

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