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Peer feedback can enhance learning but may introduce issues like peer pressure and distrust, particularly with professional competencies like teamwork. This jeopardizes the feedback process and skill development crucial for undergraduate students' career preparation. To address this, two approaches are generally used: anonymizing feedback or incorporating feedback dialogue. However, the impact of anonymity on trust and safety is unclear due to a loss of dialogue. Additionally, the effect of feedback dialogue in the context of competencies remains largely unexplored. Employing a mixed-methods approach, we divided sixty-three participants into an experimental group receiving identifiable online peer feedback with dialogue and a control group receiving anonymous feedback only. We measured students' psychological safety and trust in giving feedback on teamwork competencies, feedback quality and perceptions of the feedback process. Quantitative results showed no significant differences in safety and trust perceptions between groups, indicating that anonymity and feedback dialogue contribute to a comparably safe environment. However, the qualitative results indicated that the experimental group held more positive attitudes toward the feedback process and their feedback seemed more nuanced. This suggests that dialogue-enhanced peer feedback is preferred for fostering a safe and effective peer feedback exchange that supports professional competency development.

FLAP fingerprints are applied in the ligand-, structure- and pharmacophore-based mode in a case study on antagonists of all four adenosine receptor (AR) subtypes. Structurally diverse antagonist collections with respect to the different ARs were constructed by including binding data to human species only. FLAP models well discriminate \"active\" (=highly potent) from \"inactive\" (=weakly potent) AR antagonists, as indicated by enrichment curves, numbers of false positives, and AUC values. For all FLAP modes, model predictivity slightly decreases as follows: A(2B)R > A(2A)R > A(3)R > A(1)R antagonists. General performance of FLAP modes in this study is: ligand- > structure- > pharmacophore- based mode. We also compared the FLAP performance with other common ligand- and structure-based fingerprints. Concerning the ligand-based mode, FLAP model performance is superior to ECFP4 and ROCS for all AR subtypes. Although focusing on the early first part of the A(2A), A(2B) and A(3) enrichment curves, ECFP4 and ROCS still retain a satisfactory retrieval of actives. FLAP is also superior when comparing the structure-based mode with PLANTS and GOLD. In this study we applied for the first time the novel FLAPPharm tool for pharmacophore generation. Pharmacophore hypotheses, generated with this tool, convincingly match with formerly published data. Finally, we could demonstrate the capability of FLAP models to uncover selectivity aspects although single AR subtype models were not trained for this purpose.

Abstract Surface plasmon resonance biosensor technology (SPR) is ideally suited for fragment-based lead discovery. However, generally suitable experimental procedures or detailed protocols are lacking, especially for structurally or physico-chemically challenging targets or when tool compounds are lacking. Success depends on accounting for the features of both the target and the chemical library, purposely designing screening experiments for identification and validation of hits with desired specificity and mode-of-action, and availability of orthogonal methods capable of confirming fragment hits. By adopting a multiplexed strategy, the range of targets and libraries amenable to an SPR biosensor-based approach for identifying hits is considerably expanded. We here illustrate innovative strategies using five challenging targets and variants thereof. Two libraries of 90 and 1056 fragments were screened using two different flow-based SPR biosensor systems, allowing different experimental approaches. Practical considerations and procedures accounting for the characteristics of the proteins and libraries, and that increase robustness, sensitivity, throughput and versatility are highlighted. Competing Interest Statement Anna Moberg, Maria T. Lindgren and Claes Holmgren work for Cytiva, which produce Biacore systems.

FLAP fingerprints are applied in the ligand-, structure- and pharmacophore-based mode in a case study on antagonists of all four adenosine receptor (AR) subtypes. Structurally diverse antagonist collections with respect to the different ARs were constructed by including binding data to human species only. FLAP models well discriminate “active” (=highly potent) from “inactive” (=weakly potent) AR antagonists, as indicated by enrichment curves, numbers of false positives, and AUC values. For all FLAP modes, model predictivity slightly decreases as follows: A 2B R > A 2A R > A 3 R > A 1 R antagonists. General performance of FLAP modes in this study is: ligand- > structure- > pharmacophore- based mode. We also compared the FLAP performance with other common ligand- and structure-based fingerprints. Concerning the ligand-based mode, FLAP model performance is superior to ECFP4 and ROCS for all AR subtypes. Although focusing on the early first part of the A 2A , A 2B and A 3 enrichment curves, ECFP4 and ROCS still retain a satisfactory retrieval of actives. FLAP is also superior when comparing the structure-based mode with PLANTS and GOLD. In this study we applied for the first time the novel FLAPPharm tool for pharmacophore generation. Pharmacophore hypotheses, generated with this tool, convincingly match with formerly published data. Finally, we could demonstrate the capability of FLAP models to uncover selectivity aspects although single AR subtype models were not trained for this purpose.

FLAP fingerprints are applied in the ligand-, structure- and pharmacophore-based mode in a case study on antagonists of all four adenosine receptor (AR) subtypes. Structurally diverse antagonist collections with respect to the different ARs were constructed by including binding data to human species only. FLAP models well discriminate \"active\" (=highly potent) from \"inactive\" (=weakly potent) AR antagonists, as indicated by enrichment curves, numbers of false positives, and AUC values. For all FLAP modes, model predictivity slightly decreases as follows: A^sub 2B^R > A^sub 2A^R > A^sub 3^R > A^sub 1^R antagonists. General performance of FLAP modes in this study is: ligand- > structure- > pharmacophore- based mode. We also compared the FLAP performance with other common ligand- and structure-based fingerprints. Concerning the ligand-based mode, FLAP model performance is superior to ECFP4 and ROCS for all AR subtypes. Although focusing on the early first part of the A^sub 2A^, A^sub 2B^ and A^sub 3^ enrichment curves, ECFP4 and ROCS still retain a satisfactory retrieval of actives. FLAP is also superior when comparing the structure-based mode with PLANTS and GOLD. In this study we applied for the first time the novel FLAPPharm tool for pharmacophore generation. Pharmacophore hypotheses, generated with this tool, convincingly match with formerly published data. Finally, we could demonstrate the capability of FLAP models to uncover selectivity aspects although single AR subtype models were not trained for this purpose.[PUBLICATION ABSTRACT]