Molecular Dynamics (MD) simulations are essential in analyzing the physical movement of molecules, with GROMACS being a widely recognized open-source package for this purpose. However, conducting analyses individually in GROMACS can take excessive time and effort. Addressing this challenge, we introduce ASGARD, an innovative workflow designed to streamline and automate the analysis of MD simulation of protein or protein-ligand complex. Unlike the traditional, manual approach, ASGARD enables researchers to generate comprehensive analyses with a single command line, significantly accelerating the research process and avoiding the laborious task of manual report generation. This tool automatically performs a range of analyses post-simulation, including system stability and flexibility assessments through RMSD Fluctuation and Distribution calculations. It further provides dynamic analysis using SASA, DSSP method graphs, and various interaction analyses. A key feature of ASGARD is its user-friendly design; it requires no additional installations or dependencies, making it highly accessible for researchers. In conclusion, ASGARD simplifies the MD simulation analysis process and substantially enhances efficiency and productivity in molecular research by providing an integrated, one-command analysis solution.Communicated by Ramaswamy H. Sarma.

The development of new anticancer therapies remains challenging due to tumor heterogeneity and the frequent emergence of multidrug resistance (MDR). Natural products have garnered increasing attention as alternative or complementary therapeutic agents due to their bioactivity and reduced toxicity. Polyphenols, particularly curcumin and its derivatives, have shown promise in modulating signaling pathways, enhancing chemosensitivity, and overcoming drug resistance. The anticancer potential of dimethoxycurcumin, a chemically modified curcumin derivative identified through consensus fingerprint similarity screening, was investigated for its potential to inhibit ABCC3 (MRP3)—a member of the ATP-binding cassette (ABC) transporter family implicated in drug efflux, tumor cell survival, and resistance. In vitro experiments demonstrated that dimethoxycurcumin significantly reduced cancer cell viability and colony formation, indicating a strong inhibitory effect on ABCC3 function. These results suggest that dimethoxycurcumin may sensitize cancer cells to chemotherapy by targeting resistance pathways. The data presented contribute to the growing body of evidence suggesting that bioactive plant-derived compounds, including chemically modified derivatives, may hold therapeutic potential in oncology by modulating multidrug resistance pathways. Targeting ABC transporters with natural compound derivatives could offer a promising strategy for developing more effective and less toxic anticancer therapies.

Despite the remarkable development of highly effective vaccines, including mRNA-based vaccines, within a limited timeframe, coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is not been entirely eradicated. Thus, it is crucial to identify new effective anti-3CL^Pro compounds, pivotal for the replication of SARS-CoV-2. Here, we identified an antcin-B phytosterol-like compound from Taiwanofungus camphoratus that targets 3CL^Pro activity. MTT assay and ADMET prediction are employed for assessing potential cytotoxicity. Computational molecular modeling was used to screen various antcins and non-antcins for binding affinity and interaction type with 3CL^Pro. Further, these compounds were subjected to study their inhibitory effects on 3CL^Pro activity in vitro. Our results indicate that antcin-B has the best binding affinity by contacting residues like Leu141, Asn142, Glu166, and His163 via hydrogen bond and salt bridge and significantly inhibits 3CLPro activity, surpassing the positive control compound (GC376). The 100 ns molecular dynamics simulation studies showed that antcin-B formed consistent, long-lasting water bridges with Glu166 for their inhibitory activity. In summary, antcin-B could be useful to develop therapeutically viable drugs to inhibit SARS-CoV-2 replication alone or in combination with medications specific to other SARS-CoV-2 viral targets.

Machine learning models for chemistry require large datasets, often compiled by combining data from multiple assays. However, combining data without careful curation can introduce significant noise. While absolute values from different assays are rarely comparable, trends or differences between compounds are often assumed to be consistent. This study evaluates that assumption by analyzing potency differences between matched compound pairs across assays and assessing the impact of assay metadata curation on error reduction. We find that potency differences between matched pairs exhibit less variability than individual compound measurements, suggesting systematic assay differences may partially cancel out in paired data. Metadata curation further improves inter-assay agreement, albeit at the cost of dataset size. For minimally curated compound pairs, agreement within 0.3 pChEMBL units was found to be 44–46% for Ki and IC50 values respectively, which improved to 66–79% after curation. Similarly, the percentage of pairs with differences exceeding 1 pChEMBL unit dropped from 12 to 15% to 6–8% with extensive curation. These results establish a benchmark for expected noise in matched molecular pair data from the ChEMBL database, offering practical metrics for data quality assessment.