The Molecular Education and Research Consortium in Undergraduate Computational Chemistry (MERCURY) has supported a diverse group of faculty and students for over 20 years by providing computational resources as well as networking opportunities and professional support. The consortium comprises 38 faculty (42% women) at 34 different institutions, who have trained nearly 900 undergraduate students, more than two-thirds of whom identify as women and one-quarter identify as students of color. MERCURY provides a model for the support necessary for faculty to achieve professional advancement and career satisfaction. The range of experiences and expertise of the consortium members provides excellent networking opportunities that allow MERCURY faculty to support each other’s teaching, research, and service needs, including generating meaningful scientific advancements and outcomes with undergraduate researchers as well as being leaders at the departmental, institutional, and national levels. While all MERCURY faculty benefit from these supports, the disproportionate number of women in the consortium, relative to their representation in computational sciences generally, produces a sizable impact on advancing women in the computational sciences. In this report, the women of MERCURY share how the consortium has benefited their careers and the careers of their students.

Facing the continuous emergence of new psychoactive substances (NPS) and their threat to public health, more effective methods for NPS prediction and identification are critical. In this study, the pharmacological affinity fingerprints (Ph-fp) of NPS compounds were predicted by Random Forest classification models using bioactivity data from the ChEMBL database. The binary Ph-fp is the vector consisting of a compound’s activity against a list of molecular targets reported to be responsible for the pharmacological effects of NPS. Their performance in similarity searching and unsupervised clustering was assessed and compared to 2D structure fingerprints Morgan and MACCS (1024-bits ECFP4 and 166-bits SMARTS-based MACCS implementation of RDKit). The performance in retrieving compounds according to their pharmacological categorizations is influenced by the predicted active assay counts in Ph-fp and the choice of similarity metric. Overall, the comparative unsupervised clustering analysis suggests the use of a classification model with Morgan fingerprints as input for the construction of Ph-fp. This combination gives satisfactory clustering performance based on external and internal clustering validation indices.

In this article, we provide advice and insights, based on our own experiences, for computational chemists who are beginning new tenure-track positions at primarily undergraduate institutions. Each of us followed different routes to obtain our tenuretrack positions, but we all experienced similar challenges when getting started in our new position. In this article, we discuss our approaches to seven areas that we all found important for engaging undergraduate students in our computational chemistry research, including setting up computational resources, recruiting research students, training research students, designing student projects, managing the lab, mentoring students, and student conference participation.

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The rapid emergence of novel psychoactive substances (NPS) poses new challenges and requirements for forensic testing/analysis techniques. This paper aims to explore the application of unsupervised clustering of NPS compounds' infrared spectra. Two statistical measures, Pearson and Spearman, were used to quantify the spectral similarity and to generate the affinity matrices for hierarchical clustering. The correspondence of spectral similarity clustering trees to the commonly used structural/pharmacological categorization was evaluated and compared to the clustering generated using 2D/3D molecular fingerprints. Hybrid model feature selections were applied using different filter-based feature ranking algorithms developed for unsupervised clustering tasks. Since Spearman tends to overestimate the spectral similarity based on the overall pattern of the full spectrum, the clustering result shows the highest degree of improvement from having the non-discriminative features removed. The loading plots of the first two principal components (PCs) of the optimal feature subsets confirmed that the most important vibrational bands contributing to the clustering of NPS compounds were selected using NDFS feature selection algorithms.