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SWEETLEAD: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery.

Novick PA, Ortiz OF, Poelman J, Abdulhay AY, Pande VS - PLoS ONE (2013)

Bottom Line: Computer-aided virtual screening and repurposing approved drugs are two such strategies that have shown recent success.A consensus building scheme surveying information from several publicly accessible databases was employed to identify the correct structure for each chemical.Resulting structures are filtered for the active pharmaceutical ingredient, standardized, and differing formulations of the same drug were combined in the final database.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Stanford University, Stanford, California, United States of America.

ABSTRACT
In the face of drastically rising drug discovery costs, strategies promising to reduce development timelines and expenditures are being pursued. Computer-aided virtual screening and repurposing approved drugs are two such strategies that have shown recent success. Herein, we report the creation of a highly-curated in silico database of chemical structures representing approved drugs, chemical isolates from traditional medicinal herbs, and regulated chemicals, termed the SWEETLEAD database. The motivation for SWEETLEAD stems from the observance of conflicting information in publicly available chemical databases and the lack of a highly curated database of chemical structures for the globally approved drugs. A consensus building scheme surveying information from several publicly accessible databases was employed to identify the correct structure for each chemical. Resulting structures are filtered for the active pharmaceutical ingredient, standardized, and differing formulations of the same drug were combined in the final database. The publically available release of SWEETLEAD (https://simtk.org/home/sweetlead) provides an important tool to enable the successful completion of computer-aided repurposing and drug discovery campaigns.

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Related in: MedlinePlus

‘Drug-like’ properties of approved drugs vs. non-approved compounds in SWEETLEAD.Comparison of molecular descriptors frequently referenced as important to drug-likeness between approved drugs and other compounds in the SWEETLEAD database. The property distributions for both the approved drugs and non-approved compounds in SWEETLEAD are shown for A) molecular weight, B) the number of rotatable bonds, C) the number of hydrogen bond donors and D) acceptors.
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pone-0079568-g004: ‘Drug-like’ properties of approved drugs vs. non-approved compounds in SWEETLEAD.Comparison of molecular descriptors frequently referenced as important to drug-likeness between approved drugs and other compounds in the SWEETLEAD database. The property distributions for both the approved drugs and non-approved compounds in SWEETLEAD are shown for A) molecular weight, B) the number of rotatable bonds, C) the number of hydrogen bond donors and D) acceptors.

Mentions: The SWEETLEAD database was then constructed by inputting this list of drug and chemical names into our structure determination algorithm. For most compound names, either a consensus or majority structure was determined. Figure 3 shows the outcomes of the 1996 API names obtained from the FDA electronic Orange Book. Over 90% of names led to the identification agreed upon structure, and consensus between all databases polled was achieved for 55% of compound names. From the combined list of drug and chemical names, 4442 chemical structures were collected, and the classification of all compounds included in the initial release of SWEETLEAD is given in Table 1. Of the 4442 compounds, 2836 are approved drugs in at least one region and 1427 are FDA approved drugs. 217 of these compounds are scheduled drugs in the United States, and 1625 of the compounds derive from traditional medicinal herbs. Some compounds can be multiply classified, such as methamphetamine and codeine which are both approved therapeutics and scheduled drugs. Reflecting the fact that multiple formulations of APIs are combined into a single entry in SWEETLEAD, the number of unique PubChem IDs (4713) and ChemSpider IDs (4501) is greater than the total number of unique compounds. The entire SWEETLEAD database can be downloaded at https://simtk.org/home/sweetlead. A natural question following our decision to include approved and non-approved compounds in SWEETLEAD, and in light of often discussed ‘drug-like’ properties, would be whether the two sets of compounds are similar in their molecular properties. Figure 4 compares the distributions of molecular properties of the approved drugs and non-approved chemicals in the SWEETLEAD database. Molecular weight and rotor, hydrogen bond donor, and hydrogen bond acceptor distributions are shown, while predicted LogP was omitted due to inaccuracies in computational LogP prediction tools. Chi-Squared tests indicate that the distributions for all for properties are statistically different between the drug and non-drug groups, however this difference is most marked for the distributions of rotatable bonds where over 31% of non-drugs have less than 2 rotatable bonds as compared to 13% for approved drugs. Additionally, 93% of the non-approved compounds pass Lipinski’s Rule of 5 with only 1 violation, identical to the 93% mark for approved drugs, which indicates their drug likeness according to that metric. Overall, both the drugs and non-drugs in the SWEETLEAD database exhibit molecular properties similar to the ‘drug-like’ rules of thumb typically used in a drug discovery setting.


SWEETLEAD: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery.

Novick PA, Ortiz OF, Poelman J, Abdulhay AY, Pande VS - PLoS ONE (2013)

‘Drug-like’ properties of approved drugs vs. non-approved compounds in SWEETLEAD.Comparison of molecular descriptors frequently referenced as important to drug-likeness between approved drugs and other compounds in the SWEETLEAD database. The property distributions for both the approved drugs and non-approved compounds in SWEETLEAD are shown for A) molecular weight, B) the number of rotatable bonds, C) the number of hydrogen bond donors and D) acceptors.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3815155&req=5

pone-0079568-g004: ‘Drug-like’ properties of approved drugs vs. non-approved compounds in SWEETLEAD.Comparison of molecular descriptors frequently referenced as important to drug-likeness between approved drugs and other compounds in the SWEETLEAD database. The property distributions for both the approved drugs and non-approved compounds in SWEETLEAD are shown for A) molecular weight, B) the number of rotatable bonds, C) the number of hydrogen bond donors and D) acceptors.
Mentions: The SWEETLEAD database was then constructed by inputting this list of drug and chemical names into our structure determination algorithm. For most compound names, either a consensus or majority structure was determined. Figure 3 shows the outcomes of the 1996 API names obtained from the FDA electronic Orange Book. Over 90% of names led to the identification agreed upon structure, and consensus between all databases polled was achieved for 55% of compound names. From the combined list of drug and chemical names, 4442 chemical structures were collected, and the classification of all compounds included in the initial release of SWEETLEAD is given in Table 1. Of the 4442 compounds, 2836 are approved drugs in at least one region and 1427 are FDA approved drugs. 217 of these compounds are scheduled drugs in the United States, and 1625 of the compounds derive from traditional medicinal herbs. Some compounds can be multiply classified, such as methamphetamine and codeine which are both approved therapeutics and scheduled drugs. Reflecting the fact that multiple formulations of APIs are combined into a single entry in SWEETLEAD, the number of unique PubChem IDs (4713) and ChemSpider IDs (4501) is greater than the total number of unique compounds. The entire SWEETLEAD database can be downloaded at https://simtk.org/home/sweetlead. A natural question following our decision to include approved and non-approved compounds in SWEETLEAD, and in light of often discussed ‘drug-like’ properties, would be whether the two sets of compounds are similar in their molecular properties. Figure 4 compares the distributions of molecular properties of the approved drugs and non-approved chemicals in the SWEETLEAD database. Molecular weight and rotor, hydrogen bond donor, and hydrogen bond acceptor distributions are shown, while predicted LogP was omitted due to inaccuracies in computational LogP prediction tools. Chi-Squared tests indicate that the distributions for all for properties are statistically different between the drug and non-drug groups, however this difference is most marked for the distributions of rotatable bonds where over 31% of non-drugs have less than 2 rotatable bonds as compared to 13% for approved drugs. Additionally, 93% of the non-approved compounds pass Lipinski’s Rule of 5 with only 1 violation, identical to the 93% mark for approved drugs, which indicates their drug likeness according to that metric. Overall, both the drugs and non-drugs in the SWEETLEAD database exhibit molecular properties similar to the ‘drug-like’ rules of thumb typically used in a drug discovery setting.

Bottom Line: Computer-aided virtual screening and repurposing approved drugs are two such strategies that have shown recent success.A consensus building scheme surveying information from several publicly accessible databases was employed to identify the correct structure for each chemical.Resulting structures are filtered for the active pharmaceutical ingredient, standardized, and differing formulations of the same drug were combined in the final database.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Stanford University, Stanford, California, United States of America.

ABSTRACT
In the face of drastically rising drug discovery costs, strategies promising to reduce development timelines and expenditures are being pursued. Computer-aided virtual screening and repurposing approved drugs are two such strategies that have shown recent success. Herein, we report the creation of a highly-curated in silico database of chemical structures representing approved drugs, chemical isolates from traditional medicinal herbs, and regulated chemicals, termed the SWEETLEAD database. The motivation for SWEETLEAD stems from the observance of conflicting information in publicly available chemical databases and the lack of a highly curated database of chemical structures for the globally approved drugs. A consensus building scheme surveying information from several publicly accessible databases was employed to identify the correct structure for each chemical. Resulting structures are filtered for the active pharmaceutical ingredient, standardized, and differing formulations of the same drug were combined in the final database. The publically available release of SWEETLEAD (https://simtk.org/home/sweetlead) provides an important tool to enable the successful completion of computer-aided repurposing and drug discovery campaigns.

Show MeSH
Related in: MedlinePlus