The selection, acquisition, and use of high‐quality small molecule libraries for screening is an essential aspect of drug discovery and chemical biology programs. Screening libraries continue to evolve as researchers gain a greater appreciation of the suitability of small molecules for specific biological targets, processes, and environments. The decision surrounding the makeup of any given small molecule library is informed by a multitude of variables, and opinions vary on best practices. The fitness of any collection relies upon upfront filtering to avoid problematic compounds, assess appropriate physicochemical properties, install the ideal level of structural uniqueness, and determine the desired extent of molecular complexity. These criteria are under constant evaluation and revision as academic and industrial organizations seek out collections that yield ever‐improving results from their screening portfolios. Practical questions including cost, compound management, screening sophistication, and assay objective also play a significant role in the choice of library composition. This overview attempts to offer advice to all organizations engaged in small molecule screening based upon current best practices and theoretical considerations in library selection and acquisition. Curr. Protoc. Chem. Biol. 4:177‐191 © 2012 by John Wiley & Sons, Inc.

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

The D1 dopamine receptor (D1R) and its signaling is associated with several important neurological processes and neuropsychiatric disorders. Enhancing D1R signaling in the prefrontal cortex is associated with increased cognition and therefore is an appealing treatment strategy for the cognitive decline observed in schizophrenia, Alzheimer's disease, and other disorders. Because of the clinical liability inherent with D1R agonists, positive allosteric modulators of the D1R have been proposed as an alternative, due to their potential for high selectivity and larger therapeutic windows. Currently, the location and characteristics of potential binding site(s) for allosteric modulators on the D1R is unknown. We have identified two structurally diverse D1R positive allosteric modulators, MLS1082 and MLS6585, via a high throughput screen of the NIH Molecular Libraries Program. The compounds potentiate dopamine‐stimulated G‐protein‐ (cAMP stimulation, 3–5 fold) and β‐arrestin‐mediated (6–8 fold) signaling pathways and increase the binding affinity of dopamine for the D1R (3–6 fold). Neither compound displayed agonist activity in the absence of dopamine. Taking advantage of our structurally distinct potentiators, G‐protein signaling and β‐arrestin recruitment experiments using maximally effective concentrations of MLS6585 and MLS1082 in combination were used to determine if the compounds act at similar or separate sites. In combination, MLS1082 + MLS6585 caused an additive potentiation of the potency of dopamine beyond that caused by either compound alone for both β‐arrestin recruitment (11–20 fold) and cAMP accumulation (4–6 fold). This suggests that the two compounds are acting at separate sites on the receptor. We also observed similar results using analogs of the two compounds, with analogs of MLS6585 having additive activity with MLS1082 and vice‐versa. However, analogs are not additive with their parent compound, meaning the parent compounds and their analogs act at similar sites. The combination experiments were repeated with Compound B, a known D1R positive allosteric modulator. Compound B was additive with MLS6585 but not MLS1082, further suggesting that the D1R has two separate positive allosteric modulator binding sites. Point mutation studies are currently underway to identify the location of these putative sites as well as whether these sites may contribute separately to potentiation of signaling efficacy versus dopamine binding affinity.Support or Funding InformationNINDS IRPThis abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

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