Estrogen receptors (ERα) are a critical target for drug design as well as a potential source of toxicity when activated unintentionally. Thus, evaluating potential ERα binding agents is critical in both drug discovery and chemical toxicity areas. Using computational tools, e.g., Quantitative Structure-Activity Relationship (QSAR) models, can predict potential ERα binding agents before chemical synthesis. The purpose of this project was to develop enhanced predictive models of ERα binding agents by utilizing advanced cheminformatics tools that can integrate publicly available bioassay data. The initial ERα binding agent data set, consisting of 446 binders and 8307 non-binders, was obtained from the Tox21 Challenge project organized by the NIH Chemical Genomics Center (NCGC). After removing the duplicates and inorganic compounds, this data set was used to create a training set (259 binders and 259 non-binders). This training set was used to develop QSAR models using chemical descriptors. The resulting models were then used to predict the binding activity of 264 external compounds, which were available to us after the models were developed. The cross-validation results of training set [Correct Classification Rate (CCR) = 0.72] were much higher than the external predictivity of the unknown compounds (CCR = 0.59). To improve the conventional QSAR models, all compounds in the training set were used to search PubChem and generate a profile of their biological responses across thousands of bioassays. The most important bioassays were prioritized to generate a similarity index that was used to calculate the biosimilarity score between each two compounds. The nearest neighbors for each compound within the set were then identified and its ERα binding potential was predicted by its nearest neighbors in the training set. The hybrid model performance (CCR = 0.94 for cross validation; CCR = 0.68 for external prediction) showed significant improvement over the original QSAR models, particularly for the activity cliffs that induce prediction errors. The results of this study indicate that the response profile of chemicals from public data provides useful information for modeling and evaluation purposes. The public big data resources should be considered along with chemical structure information when predicting new compounds, such as unknown ERα binding agents.
This mixed-methods study examines the implications of using the tenets of culturally relevant pedagogy (CRP) to design an elementary science lesson grounded in four virtual reality (VR) videos. Given the need for additional understandings of how elementary science educators can infuse cultural relevance alongside content development, this study illuminates how designing for CRP can utilize VR as a pedagogical platform to bridge science instruction and students’ lived experiences. Using pre- and post-attitudinal surveys (n=145) and post interviews (n=48), we examined students’ perceptions of a single virtual reality lesson about energy and food chains. The data suggest that learning through a CRP-based VR design (CRP-VR) enhanced students’ perception of the connection between the science content and its socio-political application to social justice issues. Implications highlight the potential of leveraging VR technology as a means to provide science instruction that explicitly affords students the opportunity to connect content learning and social action.
Although the population of science teachers in the US lacks diversity, the technology used to teach science should be diverse and culturally connected. Culturally relevant frameworks for science call for science teaching to be situated in meaningful contexts. This study calls for the integration of CRP and situated cognition perspectives as a means of designing pedagogically effective virtual reality. This mixed-methods study explored N = 444 students changes in attitudes after experiencing VR lessons on an attitude survey and qualitative short answers. We found that all students who used the VR improved their attitudes towards science. We also found that students who used the CRP designed VR developed a conscious and politically aware application of science. The results suggest that educational technology should carefully consider how using CRP-based approaches can have both cognitive and cultural implications of students' learning.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.