BOC Sciences uses a range of ligand-based drug design tools to advance drug discovery projects when structural information on target proteins is lacking.
In two dimensions, structurally similar molecules may have similar biological activities by targeting the same protein. In three dimensions, molecules of similar size in active pockets of proteins may have the potential to efficiently bind to spaces of the same or similar size.
Ligand similarity comparison and search can help researchers quickly find potential compounds. BOC Sciences has accumulated rich experience in data analysis for a large number of compound collections, providing researchers with a variety of ligand similarity search programs, and performing similarity comparisons by calculating the fingerprint properties of molecules. Protein targets of matching molecules with high similarity can be considered as potential targets for search calculation molecules.
Pharmacophore is a collection of pharmacodynamic characteristic elements, which are structural features required to maintain the activity of compounds, allowing molecules to interact with target proteins in specific binding modes. These features determine the activity of the ligand and can reflect the common atom, gene or chemical functional structure and spatial orientation of the compound on the three-dimensional structure. The basic principle of pharmacophore screening is that the binding of a drug to its protein target is mainly determined by the key functional pharmacophore. Therefore, the matching of these important pharmacophores can be used to find new targets for small molecule drugs.
BOC Sciences screens drugs by analyzing the common pharmacodynamic profile of known receptor-binding ligands. Based on pharmacophore technology and comparison methods, specific pharmacophore models are constructed for comparing different series of compounds or evaluating new compounds with potential activities.
Scaffold hopping approaches start from known active compounds and obtain novel chemical structures by changing the core structure of the molecule. Finding compounds with novel structures is of great significance to the development of new drugs. Scaffold hopping approaches are not only used in projects with known ligands, but also widely used in lead compound optimization. BOC Sciences uses advanced deep learning algorithms to generate valuable new IP compounds and improve poorly-characterized chemical structures using various scaffold hopping approaches.
Scaffold hopping technology
Molecular property prediction is one of the most fundamental tasks in cheminformatics. The most common of these is the QSAR model. Based on the three-dimensional structure of the ligand and the target, QSAR fits a series of known pharmacological physicochemical properties and three-dimensional structure parameters to a quantitative relationship according to the energy change of the intramolecular energy and the energy change of the intermolecular interaction, and then Therefore, QSAR can not only simulate the structural characteristics of ligands that bind to receptors, but also predict the activity of drugs. BOC Sciences uses known experimental data and cheminformatics models (such as QSAR models, machine learning, etc.) to predict the activity and ADMET properties of new compounds.
BOC Sciences predicts the stable conformation and flexibility of the compound structure with optimal biological activity through geometric optimization and conformational analysis calculated by semi-empirical or QM methods.
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