Compared with traditional non-covalent inhibitors, covalent inhibitors can achieve higher binding affinity and longer residence time. These characteristics can be realized into lower doses, dosing frequency and systemic exposure of drugs. Therefore, many pharmaceutical companies have initiated drug development programs for covalent inhibitors of various enzymes. Once the ligand is docked into the binding site, the warhead will form a covalent bond with nearby amino acid residues. By designing inhibitors so that covalent bonds are only formed on non-catalytic residues with poor conservation, the target selectivity can be maximized. Chemistry Calculation https://www.computabio.com/calculation-service-of-addition-reaction-and-reaction-rate-constant.html

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Antibodies are proteins that play a key role in recognizing various antigens in our immune system. Antibodies are increasingly used as diagnostic tools and therapeutic drugs, and an important part of the antibody engineering process is the availability of high-quality three-dimensional structures. Experimental determination of antibody structure is laborious and not always possible. On the contrary, computational modeling provides a fast and cheap way to generate antibody structural models. Antibody Modeling https://www.computabio.com/antibody-modeling-services.html

Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. anova analysis https://www.computabio.com/anova-analysis.html

3D-QSAR refers to the application of force field calculations, requiring the three-dimensional structure of a given set of small molecules (training set) with known activities. The training set needs to be superimposed (aligned) by experimental data (based on ligand-protein crystallography) or molecular superposition software. It uses calculated potentials, such as Lennard-Jones potentials rather than experimental constants, but related to the entire molecule, rather than individual substituents. It studies the spatial field (the shape of the molecule) and the electrostatic field related to each other through partial least squares regression (PLS). 3D-QSAR https://www.computabio.com/3d-qsar-service.html

The quantitative structure-activity relationship (QSAR) models describe the relationship between molecular structure and a certain biological activity of the molecule. The basic assumption is that the molecular structure of a compound contains information that determines its physical, chemical, and biological properties, and these physical and chemical properties further determine the biological activity of the compound. Furthermore, the molecular structure property data of the compound and its biological activity should also be related to some extent. 2D-QSAR https://www.computabio.com/2d-qsar-service.html