High-Throughput Gene Expression Profiles to Define Drug Similarity and Predict Compound Activity.

By adding biological information, beyond the chemical properties and desired effect of a compound, uncharted compound areas and connections can be explored. In this study, we add transcriptional information for 31K compounds of Janssen's primary screening deck, using the HT L1000 platform and assess (a) the transcriptional connection score for generating compound similarities, (b) machine learning algorithms for generating target activity predictions, and (c) the scaffold hopping potential of the resulting hits. We demonstrate that the transcriptional connection score is best computed from the significant genes only and should be interpreted within its confidence interval for which we provide the stats. These guidelines help to reduce noise, increase reproducibility, and enable the separation of specific and promiscuous compounds. The added value of machine learning is demonstrated for the NR3C1 and HSP90 targets. Support Vector Machine models yielded balanced accuracy values ≥80% when the expression values from DDIT4 & SERPINE1 and TMEM97 & SPR were used to predict the NR3C1 and HSP90 activity, respectively. Combining both models resulted in 22 new and confirmed HSP90-independent NR3C1 inhibitors, providing two scaffolds (i.e., pyrimidine and pyrazolo-pyrimidine), which could potentially be of interest in the treatment of depression (i.e., inhibiting the glucocorticoid receptor (i.e., NR3C1), while leaving its chaperone, HSP90, unaffected). As such, the initial hit rate increased by a factor 300, as less, but more specific chemistry could be screened, based on the upfront computed activity predictions.

Chemogenomics approaches for receptor deorphanization and extensions of the chemogenomics concept to phenotypic space.

Chemogenomic approaches, which link ligand chemistry to bioactivity against targets (and, by extension, to phenotypes) are becoming more and more important due to the increasing number of bioactivity data available both in proprietary databases as well as in the public domain. In this article we review chemogenomics approaches applied in four different domains: Firstly, due to the relationship between protein targets from which an approximate relation between their respective bioactive ligands can be inferred, we investigate the extent to which chemogenomics approaches can be applied to receptor deorphanization. In this case it was found that by using knowledge about active compounds of related proteins, in 93% of all cases enrichment better than random could be obtained. Secondly, we analyze different cheminformatics analysis methods with respect to their behavior in chemogenomics studies, such as subgraph mining and Bayesian models. Thirdly, we illustrate how chemogenomics, in its particular flavor of 'proteochemometrics', can be applied to extrapolate bioactivity predictions from given data points to related targets. Finally, we extend the concept of 'chemogenomics' approaches, relating ligand chemistry to bioactivity against related targets, into phenotypic space which then falls into the area of 'chemical genomics' and 'chemical genetics'; given that this is very often the desired endpoint of approaches in not only the pharmaceutical industry, but also in academic probe discovery, this is often the endpoint the experimental scientist is most interested in.