To the editor: With the rapid accumulation of data in all areas of chemical biology research, sci... more To the editor: With the rapid accumulation of data in all areas of chemical biology research, scientists rely increasingly on historical chemogenomics data and computational models to guide small-molecule bioactivity screens and chemical probe development. However, there is a growing public concern about the frequent irreproducibility of experimental data reported in peer-reviewed scientific publications 1,2 . An editorial in this journal 3 emphasized a critical need to address this problem, an issue that has also received attention from the US National Institutes of Health (NIH) leadership 4 . Since successful development of chemical probes and reliable screening assays-one central objective of chemical biology-rely on the prior art in the field, it is critical that researchers establish the highest possible quality standards for data deposited in chemogenomics databases.
In this chapter, we discuss the application of modern cheminformatics approaches such as Quantita... more In this chapter, we discuss the application of modern cheminformatics approaches such as Quantitative Structure – Activity Relationships (QSAR) modeling towards developing computational tools capable of forecasting the biological effects of Manufactured NanoParticles (MNPs) solely from the knowledge of their physical, chemical, and geometrical properties; by analogy with QSAR we term such approach Quantitative Nanostructure – Activity Relationships (QNAR) modelling. First, we stress the difficulties of compiling, curating, and integrating experimental MNP related data from different sources. We illustrate those difficulties with the case study of automatic and manual literature mining for CeO2 nanoparticles. Second, we discuss the major approaches for QNAR model building and validation using both experimental and computed properties of nanomaterials. We consider two different categories of MNP datasets: (i) those comprising MNPs with diverse metal cores and different surface-modifyi...
Only five isoforms (1A2, 2C9, 2C19, 2D6, and 3A4) of Cytochrome P450 (CYP) enzymes are responsibl... more Only five isoforms (1A2, 2C9, 2C19, 2D6, and 3A4) of Cytochrome P450 (CYP) enzymes are responsible for the metabolism of the vast majority of drugs. Beyond knowing what drugs are metabolized by a specific isoform and the subsequent metabolites, it is of critical importance to evaluate the CYP inhibition potency of a compound for predicting its most likely CYP-related adverse effects. In this study, our objective was to integrate, analyze, and model the growing compendium of publicly-available CYP-related datasets for these five isoforms using cheminformatics approaches: INSTEM (4,676 compounds) was obtained as assertional meta-data generated from MEDLINE abstracts; SUPERCYP (678 compounds) extracted from the online webportal; NCGC (16,142 compounds) is a compilation of qHTS screening results released by the NIH Center for Translational Technologies. After chemical curation and duplicates' analysis, we have analyzed these datasets using cheminformatics approaches and addressed qu...
tagonists versus non-antagonists. Selected models with the best external prediction performances ... more tagonists versus non-antagonists. Selected models with the best external prediction performances were applied to a library of ca. 1000 chemicals with known side effects profiles in order to predict their potential 5-HT6 binding and/or dopamine antagonism. In each case the virtual screening process was able to identify putatively active compounds that through subsequent literature-based validation were found to be likely or known 5-HT6 binders or dopamine antagonists. These results demonstrate that side effect profiles can be utilized to predict a drug's unknown molecular activity, thus representing a valuable opportunity in repositioning the drug for a new indications.
Quantitative structure-activity relationship (QSAR) models have been developed for a dataset of 3... more Quantitative structure-activity relationship (QSAR) models have been developed for a dataset of 3133 compounds defined as either active or inactive against P. falciparum. Since the dataset was strongly biased towards inactive compounds, different sampling approaches were employed to balance the ratio of actives vs. inactives, and models were rigorously validated using both internal and external validation approaches. The balanced accuracy for assessing the antimalarial activities of 70 external compounds was between 87% and 100% depending on the approach used to balance the dataset. Virtual screening of the ChemBridge database using QSAR models identified 176 putative antimalarial compounds that were submitted for experimental validation, along with 42 putative inactives as negative controls. Twenty five (14.2%) computational hits were found to have antimalarial activities with minimal cytotoxicity to mammalian cells, while all 42 putative inactives were confirmed experimentally. Structural inspection of confirmed active hits revealed novel chemical scaffolds, which could be employed as starting points to discover novel antimalarial agents.
We have compiled, curated, and integrated the largest publicly-accessible datasets of human skin ... more We have compiled, curated, and integrated the largest publicly-accessible datasets of human skin sensitization (381 compounds) and skin permeability (186 and 96 compounds for human and rodents, respectively). For both end points, we have built rigorously validated QSAR models using Random Forest with SiRMS and Dragon molecular descriptors. These models showed reasonable (53-62%) coverage and external predictive power as high as CCRext = 80% for skin sensitization and Q2ext= 0.87-0.89 for human and rodent skin permeability; models are available on the Chembench webportal (http://chembench.mml.unc.edu/). Virtual screening of the ToxCast chemical library using selected QSAR models identified 56 compounds as potential skin sensitizers. Furthermore, we have explored a hypothesis that skin permeability and sensitization could be related. The concordance analysis showed no significant global correlation; however, clustering chemicals by structural similarity revealed that the correlation d...
To the editor: With the rapid accumulation of data in all areas of chemical biology research, sci... more To the editor: With the rapid accumulation of data in all areas of chemical biology research, scientists rely increasingly on historical chemogenomics data and computational models to guide small-molecule bioactivity screens and chemical probe development. However, there is a growing public concern about the frequent irreproducibility of experimental data reported in peer-reviewed scientific publications 1,2 . An editorial in this journal 3 emphasized a critical need to address this problem, an issue that has also received attention from the US National Institutes of Health (NIH) leadership 4 . Since successful development of chemical probes and reliable screening assays-one central objective of chemical biology-rely on the prior art in the field, it is critical that researchers establish the highest possible quality standards for data deposited in chemogenomics databases.
In this chapter, we discuss the application of modern cheminformatics approaches such as Quantita... more In this chapter, we discuss the application of modern cheminformatics approaches such as Quantitative Structure – Activity Relationships (QSAR) modeling towards developing computational tools capable of forecasting the biological effects of Manufactured NanoParticles (MNPs) solely from the knowledge of their physical, chemical, and geometrical properties; by analogy with QSAR we term such approach Quantitative Nanostructure – Activity Relationships (QNAR) modelling. First, we stress the difficulties of compiling, curating, and integrating experimental MNP related data from different sources. We illustrate those difficulties with the case study of automatic and manual literature mining for CeO2 nanoparticles. Second, we discuss the major approaches for QNAR model building and validation using both experimental and computed properties of nanomaterials. We consider two different categories of MNP datasets: (i) those comprising MNPs with diverse metal cores and different surface-modifyi...
Only five isoforms (1A2, 2C9, 2C19, 2D6, and 3A4) of Cytochrome P450 (CYP) enzymes are responsibl... more Only five isoforms (1A2, 2C9, 2C19, 2D6, and 3A4) of Cytochrome P450 (CYP) enzymes are responsible for the metabolism of the vast majority of drugs. Beyond knowing what drugs are metabolized by a specific isoform and the subsequent metabolites, it is of critical importance to evaluate the CYP inhibition potency of a compound for predicting its most likely CYP-related adverse effects. In this study, our objective was to integrate, analyze, and model the growing compendium of publicly-available CYP-related datasets for these five isoforms using cheminformatics approaches: INSTEM (4,676 compounds) was obtained as assertional meta-data generated from MEDLINE abstracts; SUPERCYP (678 compounds) extracted from the online webportal; NCGC (16,142 compounds) is a compilation of qHTS screening results released by the NIH Center for Translational Technologies. After chemical curation and duplicates' analysis, we have analyzed these datasets using cheminformatics approaches and addressed qu...
tagonists versus non-antagonists. Selected models with the best external prediction performances ... more tagonists versus non-antagonists. Selected models with the best external prediction performances were applied to a library of ca. 1000 chemicals with known side effects profiles in order to predict their potential 5-HT6 binding and/or dopamine antagonism. In each case the virtual screening process was able to identify putatively active compounds that through subsequent literature-based validation were found to be likely or known 5-HT6 binders or dopamine antagonists. These results demonstrate that side effect profiles can be utilized to predict a drug's unknown molecular activity, thus representing a valuable opportunity in repositioning the drug for a new indications.
Quantitative structure-activity relationship (QSAR) models have been developed for a dataset of 3... more Quantitative structure-activity relationship (QSAR) models have been developed for a dataset of 3133 compounds defined as either active or inactive against P. falciparum. Since the dataset was strongly biased towards inactive compounds, different sampling approaches were employed to balance the ratio of actives vs. inactives, and models were rigorously validated using both internal and external validation approaches. The balanced accuracy for assessing the antimalarial activities of 70 external compounds was between 87% and 100% depending on the approach used to balance the dataset. Virtual screening of the ChemBridge database using QSAR models identified 176 putative antimalarial compounds that were submitted for experimental validation, along with 42 putative inactives as negative controls. Twenty five (14.2%) computational hits were found to have antimalarial activities with minimal cytotoxicity to mammalian cells, while all 42 putative inactives were confirmed experimentally. Structural inspection of confirmed active hits revealed novel chemical scaffolds, which could be employed as starting points to discover novel antimalarial agents.
We have compiled, curated, and integrated the largest publicly-accessible datasets of human skin ... more We have compiled, curated, and integrated the largest publicly-accessible datasets of human skin sensitization (381 compounds) and skin permeability (186 and 96 compounds for human and rodents, respectively). For both end points, we have built rigorously validated QSAR models using Random Forest with SiRMS and Dragon molecular descriptors. These models showed reasonable (53-62%) coverage and external predictive power as high as CCRext = 80% for skin sensitization and Q2ext= 0.87-0.89 for human and rodent skin permeability; models are available on the Chembench webportal (http://chembench.mml.unc.edu/). Virtual screening of the ToxCast chemical library using selected QSAR models identified 56 compounds as potential skin sensitizers. Furthermore, we have explored a hypothesis that skin permeability and sensitization could be related. The concordance analysis showed no significant global correlation; however, clustering chemicals by structural similarity revealed that the correlation d...
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Papers by Denis Fourches