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Finding a Job That Fits Your Interests Think about what you are good at. Usually the things we love are the things we are good at. Research the fields and options that best deal with those interests so you can spend your time at work doing things that you enjoy. To begin, you can start off by simply researching job titles online or you can search for keywords of things you enjoy to find jobs that integrate those things. Consider your current hobbies and how you can apply them to a job. Think about your past work experience and the things you liked and didn't like about them. Consider working with a career coach at a local university if you are a student. This will help you to figure out what you are good at. spacebar clicker

I couldn't contain my excitement, so I had to share this amazing news with you all. I recently had the privilege of attending Vox Live, and let me tell you, it was an experience like no other! From the moment I stepped into the venue, I was captivated by the electrifying atmosphere and the anticipation in the air.

Prediction of ADMET (absorption, distribution, metabolism, excretion, and toxicity) is an essential step in drug design and drug screening. The ADME absorption distribution metabolism and excretion is considered a chemical compound within an organism. ADME/Tox prediction has been applied to investigate the drug levels and kinetics of candidates when it exposures to the tissues. Scientists can modify the pharmacological activity of the compound by predicting these characteristics rapidly. For more: https://drugscreening.bocsci.com/services/admet-modeling-and-prediction.html Prediction Workflow At BOC Sciences Data collection We collect data from various public databases through manually filtering and multi-step processing. Data preparation We perform pretreatments by removing compounds without detailed and reliable information of ADME/Tox properties. Descriptor Selection Our scientists develop a validated regression model by filtering descriptors with invalid information or intrusive. Modeling At BOC Sciences, two kinds of computational approaches are applied: Molecular modelling and data modelling. Evaluation We generate a set of prediction models with the application of validation approaches and crucial parameters to evaluate various ADMET properties. Our Abilities of ADMET Modeling and Prediction RF, SVM, RP and PLS are used for regression model building. RF, SVM, NB and DT are applied to build those classification models. Some basic physiochemical properties: Aqueous (water) solubility, boiling point, logD, logP, LogD7.4, pKa, MW, number of hydrogen bond donors/acceptors and rotatable bonds, molar refractivity/volume, polarizability. Absorption: Intestinal absorption, Caco-2 permeability, Pgp-Inhibitor/substrate, HIA, oral bioavailability Distribution: Plasma protein binding, Vd, P-gp, AUC, PPB, VD, BBB. Metabolism: Half-time, CYP-Inhibitor/substrate, drug-drug interaction. Excretion: Urinary excretion, clearance, DILI Toxicity: Acute/aquatic toxicity, genotoxicity, hERG Inhibition, H-HT, Ames, SkinSen. Molecular modelling: We apply protein modeling when the 3D structure of proteins are known as well as homology modelling and pharmacohphore models while the protein structure is not available. Data modelling: At BOC Sciences, we introduce QSAR and QSPR approaches to investigate a variety of biological and physicochemical data by searching for correlations between a given property and a set of molecular of structural descriptors of the molecules. We can reduce the random error when fluctuations of compounds values in a reasonable limit wash molecules by molecular operating environment software. Multiple widely used molecular descriptors are available for further model building including constitution, topology, connectivity, E-state, Kappa, basak, burden, autocorrelation, charge property, MOE-type descriptors, etc. Different modeling algorithms are applied to develop regression or classification models for ADME/T related properties: We are capable of evaluating various predicted properties including: Our computational modeling capabilities Our Advantages of ADMET Modeling and Prediction We offer accurate and liable predicted ADME-Tox data to assist your virtual screening. Our groups are confident in the estimation for classification models. At our ADME/Tox prediction platforms, other properties such as bioavailability, safety, as well as activity, are able to be investigated in parallel. We have extensive experience in pharmacophore modeling and QSAR modeling, delivering high-quality customized models. We are committed in the development of more effective ADME-Tox models utilizing large and well-validated datasets.

Antibody-drug conjugates (ADCs) are composed of monoclonal antibodies targeting specific antigens and small-molecule cytotoxic drugs linked by linkers, which have both the powerful killing effect of traditional small-molecule chemotherapy and the tumor targeting of antibody drugs. Up to date, 14 antibody-drug conjugates (ADCs) have been approved for the treatment of various cancers, and approximately 100 ADC candidates are in clinical trials worldwide. For details: https://www.solutions.bocsci.com/antibody-drug-conjugate.html Over recent years, studies have found that the site-specific ADC drugs formed by quantitatively coupling small-molecule drugs to specific sites of antibodies have a better therapeutic index, and have gradually become the focus of research and development in the field of ADC drugs. However, due to the complex amino acid composition of antibodies, site-specific conjugation of small-molecule drugs by chemical methods has always been challenging. Among them, ligand-directed site-specific coupling technology is a potential method, but the difficult release of redundant ligands or the complex release process limits the application of this technology in site-specific ADC drug development. Recently, Huang group from Shanghai Institute of Materia Medica Chinese Academy of Sciences developed a novel ligand-directed acylation reagent based on a thioester structure, which can automatically release redundant ligand structures while site-directed modification of lysines at specific sites of antibodies, and realize "one-step", "no trace" preparation of site-directed antibody drug conjugates for the first time by chemical methods, providing a novel preparation for the development of targeted ADC drugs. The research team applied this technology to realize various forms of functional molecular modification, such as the "one-step" site-specific azation, biotinylation and various small molecule drugs, all of which were specifically coupled to two molecular compounds. This traceless method has demonstrated excellent efficiency in one-step IgG conjugation with various cargoes including azide (Fig.2), biotin (Fig.3), toxins (Fig.4), etc. Analysis methods such as high-resolution mass spectrometry and mass spectrometry proteomics have proved that the small molecule modification occurs at the targeted site in the Fc region of the antibody, with extremely high site selectivity. Meanwhile, this technology is also applicable to IgG1 antibodies such as rituximab, pertuzumab, bevacizumab, IgG2 antibodies such as panitumumab, and IgG4 antibodies such as nivolumab, and shows good substrate universality. Subsequently, the team prepared four "one-step" site-directed ADC compounds with different drug linkers based on trastuzumab, and three "two-step" site-directed ADC compounds based on bioorthogonal reactions, and carried out the preliminary pharmaceutical evaluation. All ADC compounds exhibited good structural homogeneity (DAR=2), strong in vitro tumor cell inhibitory activity (<0.1 nM) and very low cytotoxicity (≥1000 nM). At the same time, compared with the positive ADC compounds prepared by random coupling (DAR≈3.3), the multiple site-directed ADC compounds obtained by this technique have lower drug loadings (DAR=2), but show stronger tumor in vivo inhibitory activity. In conclusion, the research team has realized the "one-step" and "no trace" site-specific quantitative modification of lysine at specific sites of natural antibodies by designing novel acylation reagents and optimizing the structure of ligands. For the first time, this technology realizes the efficient and uniform preparation of site-directed ADC compounds by chemical means without the need for antibody engineering and bioorthogonal reactions, and is compatible with diverse substrate structures and antibody types, which exhibits broad potential applications in ligand-directed protein modifications.