Boosting the Accuracy and Chemical Space Coverage of the Detection of Small Colloidal Aggregating Molecules Using the BAD Molecule Filter.

The ability to conduct effective high throughput screening (HTS) campaigns in drug discovery is often hampered by the detection of false positives in these assays due to small colloidally aggregating molecules (SCAMs). SCAMs can produce artifactual hits in HTS by nonspecific inhibition of the protein target. In this work, we present a new computational prediction tool for detecting SCAMs based on their 2D chemical structure. The tool, called the boosted aggregation detection (BAD) molecule filter, employs decision tree ensemble methods, namely, the CatBoost classifier and the light gradient-boosting machine, to significantly improve the detection of SCAMs. In developing the filter, we explore models trained on individual data sets, a consensus approach using these models, and, third, a merged data set approach, each tailored for specific drug discovery needs. The individual data set method emerged as most effective, achieving 93% sensitivity and 90% specificity, outperforming existing state-of-the-art models by 20 and 5%, respectively. The consensus models offer broader chemical space coverage, exceeding 90% for all testing sets. This feature is an important aspect particularly for early stage medicinal chemistry projects, and provides information on applicability domain. Meanwhile, the merged data set models demonstrated robust performance, with a notable sensitivity of 79% in the comprehensive 10-fold cross-validation test set. A SHAP analysis of model features indicates the importance of hydrophobicity and molecular complexity as primary factors influencing the aggregation propensity. The BAD molecule filter is readily accessible for the public usage on https://molmodlab-aau.com/Tools.html. This filter provides a new, more robust tool for aggregate prediction in the early stages of drug discovery to optimize hit rates and reduce associated testing and validation overheads.

Are we ready yet for digital transformation? Virtual versus on-campus OSCE as assessment tools in pharmacy education. A randomized controlled head-to-head comparative assessment.

Background: The global COVID-19 pandemic has influenced pharmacy education including learning, assessment, and exams. In the UAE, pharmacy instructors have adapted several innovative teaching methods to strive for quality learning outcomes. The current trial presented a head-to-head comparative assessment between on-campus versus virtual Objective Structured Clinical Examination (OSCE) with examiners' and students' perspectives. Aim: The main aim was to compare fourth-year students' and examiners' perceptions of the feasibility (time and logistics), stress, performance, and satisfaction between on-campus versus virtual OSCE. Method: A randomized controlled head-to-head comparative assessment between the On-campus and virtual OSCE was conducted to explore performance and satisfaction of pharmacy students and examiners towards the two OSCE settings. The virtual OSCE was carried out directly after the on-campus -OSCE and the setting was designed in a way that aligned with the on-campus OSCE but in a virtual way. Microsoft Teams® breakout room was used as a virtual stations. Respondus-lockdown-browse and Google Meet® were used for proctoring purposes. Results: Students who sat for the on-campus assessment were more satisfied with the instructions, the orientation session, the time management, and the overall exam setting, the ability of the exam to assess their communication and clinical skills, professionalism and attitude, and the interactivity of the exam compared to the students who sat for the virtual assessment. Examiners' perceptions for both settings were the same with the exception of interaction with students (p less than 0.05) as the on-campus OSCE was more interactive. Conclusion: Students still prefer the on-campus OSCE to the virtual OSCE format in many aspects. Nevertheless, virtual OSCE is still a feasible and satisfactory method of assessment when on-campus OSCE is not possible. There is a need of a specialized platform to conduct the virtual OSCE from A to Z rather than maximizing the use of options in the current digital platforms.