Tuesday, December 07, 2010
Drug- and Lead-likeness, Target Class, and Molecular Diversity Analysis of Commercially Available Organic Compounds Provided by 29 Suppliers
Authors: A. Chuprina, O. Lukin, R. Demoiseaux, A. Buzko, and A. Shivanyuk
Source: J. Chem. Inf. Model, 2010, 50, pp. 470 – 479.
The purpose of this paper is to determine how many molecules have drug- or lead-likeness in the chemical suppliers' repertoire of compounds. The dataset starts from 7.9 million commercially available compounds whereas 5.2 million compounds were considered distinct. MySQL is the database used to store the structures, calculations parameters, and suppliers' information. The compounds were represented by SMILES strings that were converted using the JChem program. The physicochemical properties calculation was done using LigPrep and QikProp whereas biological activities used the PASS software. PASS software is a predictive algorithm based on a structural similarity search against approximately 60,000 known biologically active compounds. JChem chemical fingerprint software was utilized to perform the cluster analysis using "sphere exclusion" method. The 5.2 million compounds underwent specific filtering criteria depending on whether the inquiry was drug-like or lead-like feasibility.
The drug-like filters were based on Lipinski and Veber rules. These include logP, logS, membrane permeability, molecular weight, number of hydrogen-bond donors and acceptors, lipophilicity, and available polar surface area and rotatable bonds. Also, reactive or toxic functional groups were filtered as well. The suppliers' collection of compounds was delineated based on molecular weight, log S, and Clog P of the filtered compounds. In other words, comparison of each suppliers own assemblage showed that there was a hierarchy based on the percentage of molecules with passable scores. Overall, the percentage of compounds with drug-like characteristics has increased since 2004 which was the last time a similar search was reported.
The lead-like filters were chosen based on properties from the Hann and Oprea paper. The criteria are 200 < MW < 460, -4 < ClogP < 4.2, Hacc ≤ 9, Hdon ≤ 5, rotating bonds ≤ 10, PSA ≤ 170, CACO-2 ≥ 100, and -5 < log S < 0.5. Following the criteria for drug-likeness, the selection of any compounds containing toxic or reactive functional groups was excluded as well. The cutoff for lead-like filters did not leave room for any variation when compared to the drug-like filters. Chemical leads do not necessarily have significant biological activity and will undergo improvement of physical properties through SAR (structural activity relationship) in the lab. This strict screening of chemical attributes lead to a smaller number of compounds.
In conclusion, the 10 largest suppliers possess about 90% of the lead-like and drug-like compounds available. With respect to biological activities, the suppliers' molecules have a propensity to be active ones. It was evident that the suppliers also focused on providing more structural diversity in their stock. In addition, suppliers also directed their efforts to producing more drug-like compounds rather than lead-like compounds. The accessibility of having a wide and variable choice of readily available compounds to test will hopefully aid the researcher to be more efficient when finding appropriate leads in new research projects.
Source: J. Chem. Inf. Model, 2010, 50, pp. 470 – 479.
The purpose of this paper is to determine how many molecules have drug- or lead-likeness in the chemical suppliers' repertoire of compounds. The dataset starts from 7.9 million commercially available compounds whereas 5.2 million compounds were considered distinct. MySQL is the database used to store the structures, calculations parameters, and suppliers' information. The compounds were represented by SMILES strings that were converted using the JChem program. The physicochemical properties calculation was done using LigPrep and QikProp whereas biological activities used the PASS software. PASS software is a predictive algorithm based on a structural similarity search against approximately 60,000 known biologically active compounds. JChem chemical fingerprint software was utilized to perform the cluster analysis using "sphere exclusion" method. The 5.2 million compounds underwent specific filtering criteria depending on whether the inquiry was drug-like or lead-like feasibility.
The drug-like filters were based on Lipinski and Veber rules. These include logP, logS, membrane permeability, molecular weight, number of hydrogen-bond donors and acceptors, lipophilicity, and available polar surface area and rotatable bonds. Also, reactive or toxic functional groups were filtered as well. The suppliers' collection of compounds was delineated based on molecular weight, log S, and Clog P of the filtered compounds. In other words, comparison of each suppliers own assemblage showed that there was a hierarchy based on the percentage of molecules with passable scores. Overall, the percentage of compounds with drug-like characteristics has increased since 2004 which was the last time a similar search was reported.
The lead-like filters were chosen based on properties from the Hann and Oprea paper. The criteria are 200 < MW < 460, -4 < ClogP < 4.2, Hacc ≤ 9, Hdon ≤ 5, rotating bonds ≤ 10, PSA ≤ 170, CACO-2 ≥ 100, and -5 < log S < 0.5. Following the criteria for drug-likeness, the selection of any compounds containing toxic or reactive functional groups was excluded as well. The cutoff for lead-like filters did not leave room for any variation when compared to the drug-like filters. Chemical leads do not necessarily have significant biological activity and will undergo improvement of physical properties through SAR (structural activity relationship) in the lab. This strict screening of chemical attributes lead to a smaller number of compounds.
In conclusion, the 10 largest suppliers possess about 90% of the lead-like and drug-like compounds available. With respect to biological activities, the suppliers' molecules have a propensity to be active ones. It was evident that the suppliers also focused on providing more structural diversity in their stock. In addition, suppliers also directed their efforts to producing more drug-like compounds rather than lead-like compounds. The accessibility of having a wide and variable choice of readily available compounds to test will hopefully aid the researcher to be more efficient when finding appropriate leads in new research projects.
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