Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data.

Eight software applications are compared for their performance in estimating the octanol-water partition coefficient (Kow), melting point, vapor pressure and water solubility for a dataset of polychlorinated biphenyls, polybrominated diphenyl ethers, polychlorinated dibenzodioxins, and polycyclic aromatic hydrocarbons. The predicted property values are compared against a curated dataset of measured property values compiled from the scientific literature with careful consideration given to the analytical methods used for property measurements of these hydrophobic chemicals. The variability in the predicted values from different calculators generally increases for higher values of Kow and melting point and for lower values of water solubility and vapor pressure. For each property, no individual calculator outperforms the others for all four of the chemical classes included in the analysis. Because calculator performance varies based on chemical class and property value, the geometric mean and the median of the calculated values from multiple calculators that use different estimation algorithms are recommended as more reliable estimates of the property value than the value from any single calculator.

β-Thalassemia is an inherited hematologic disorder caused by various mutations of the β-globin gene, thus resulting in a significant decrease in adult hemoglobin (HbA) production. An increase in fetal hemoglobin (HbF) levels by drug molecules is considered of great potential in β-thalassemia treatment and is expected to counterbalance the impaired production of HbA. In this work, based on a set of 129 experimentally tested biological inhibitors, we developed and validated a computational model for the prediction of K562 functional inhibition, possibly associated with HbF induction. To facilitate future advancements in the field, we incorporated our model into Enalos Cloud Platform, which enabled online access to our computational scheme (http://enalos.insilicotox.com/K562) through a user-friendly interface. This web service is offered to the wider community to promote in silico drug discovery through fast and reliable predictions.