Nisoldipine

Non-targeted anal. (NTA) methods have been increasingly used to discover organic contaminants in environmental samples.The identification efficiency of conventional NTA workflow is often very low due to many factors such as low fragmentation score between exptl. MS2 data and reference MS2 data/in-silico MS2 data.Addnl., the prioritisation of possible candidates is also challenging due to the large number of candidates obtained from MS acquisitions.In this study, a multiplatform data processing workflow applicable to non-targeted LC-MS data is proposed to improve the identification accuracy of organic contaminants in water.This workflow consists of (1) scanning quadrupole data-independent acquisition (SONAR) to acquire relatively clean MS1 and MS2 data, (2) data deconvolution using Progenesis QI Software to filter/select the appropriate ions (MS1 data), (3) the selected MS1 data matching against U.S. EPA Chem. Dashboard to get a list of the environmentally related candidates with metadata (e.g., data sources, toxicity, and exposure), (4) exptl. MS2 data matching against in-silico fragmentation data of the obtained candidate by MetFrag to obtain a fragmentation score, (5) compound identification through ranking the candidates by MetFrag based on fragmentation score and metadata.The identification efficiency of the metadata-based NTA workflow was evaluated using a 33-component mixture of chems. in a complex sludge water.Out of 33 spiked compounds, 26 compounds had been identified using the defined NTA workflow.Most of the 26 identified compounds were recognized as 1st hit of the candidates obtained from the library searching.Overall, the incorporation of fragmentation score and metadata to rank the candidates has significantly improved the accuracy of the organic contaminant identification in the sludge water, compared to the conventional NTA workflow based solely on the fragmentation score.

Cancer remains one of the leading causes of death worldwide, with the rising incidence of breast cancer being a significant public health concern. Poly (ADP-ribose) polymerase-1 (PARP-1) has emerged as a promising therapeutic target for breast cancer treatment due to its crucial role in DNA repair. This study aimed to discover novel, targeted, and non-toxic PARP-1 inhibitors using an integrated approach that combines machine learning-based screening, molecular docking simulations, and quantum mechanical calculations. We trained a widely used machine learning models, Random Forest, using bioactivity data from known PARP-1 inhibitors. After evaluating the performance, it was used to screen an FDA-approved drug library, successfully identifying Atazanavir, Brexpiprazole, Raltegravir, and Nisoldipine as potential PARP-1 inhibitors. These compounds were further validated through molecular docking and all-atom molecular dynamics simulations, highlighting their potential for breast cancer therapy. The binding free energies indicated that Atazanavir at - 41.86 kJ/mol and Brexpiprazole at - 45.44 kJ/mol exhibited superior binding affinity compared to the control drug at - 30.42 kJ/mol, highlighting their promise as candidates for breast cancer therapy. Subsequent optimized geometries and electron density mappings of the two molecular structures revealed a Gibbs free energy of - 2334.610 Ha for the first molecule and - 1682.278316 Ha for the second, confirming enhanced stability compared to the standard drug. This study not only highlights the efficacy of machine learning in drug discovery but also underscores the importance of quantum mechanics in validating molecular stability, setting a robust foundation for future pharmacological explorations. Additionally, this approach could revolutionize the drug repurposing process by significantly reducing the time and cost associated with traditional drug development methods. Our results establish a promising basis for subsequent research aimed at optimizing these PARP-1 inhibitors for clinical use, potentially offering more effective treatment options for breast cancer patients.