Project Overview
3D-QSAR (three-dimensional quantitative structure-activity relationship) technology plays a key role in computational chemistry, its main goal is to predict the activity and other structure-related properties of molecules in organisms. This technique is based on the central premise that there is a quantifiable correlation between the mathematical characterization of molecular structure and its biological activity. In the application of 3D-QSAR, chemical descriptors (such as molecular configuration and physicochemical properties) are used as the basis for analysis, which are then combined with the bioactive parameters of the molecule (such as efficacy and toxicity). The 3D-QSAR method can predict the biological activity of unknown compounds by employing a variety of mathematical models, such as linear regression, support vector machines or neural networks. This technology has demonstrated its value in many fields such as drug development, environmental risk assessment and agricultural chemical development, providing scientists with an efficient tool that not only speeds up the screening and design process for specific bioactive compounds, but also reduces experimental costs and dependence on animal experiments.
Core advantage
Our team is equipped with a variety of state-of-the-art related software and driven by a team of experienced engineers with exceptional capabilities in efficient molecular alignment and precise descriptor selection. Our team of specialists quickly captures and analyzes the 3D properties of molecules to create accurate and highly reliable models. Our services not only focus on model accuracy, but also provide a high degree of flexibility and customization options to ensure that we can meet the specific needs of different customers.
Classic case
In the 3D-QSAR modeling process, we investigated the three-dimensional structural characteristics of 20 drug molecules, including ALogP, molecular weight, number of rings (including aromatic rings), number of hydrogen bond donors and acceptors, number of flexible bonds, polarized surface area of molecular fragments, torsional energy, solvent accessible surface area, molecular volume, and other relevant molecular fingerprint information. These characteristics relate to the number of times a drug molecule is solubilized by a particular solvent. Our model successfully predicts the solubilization factor of a drug molecule in a specific solubilizer, and the error between the predicted results and experimental data and other simulated values is within 5%, demonstrating the accuracy and reliability of the model.