Ligand-Based Virtual Screening
Ligand-based virtual screening uses structural information of known ligands or active compounds to identify similar molecules with therapeutic potential.
- The process initiates with the selection of active compounds or a query molecule known to bind to the target protein. Subsequently, molecular descriptors such as hydrogen bonding, hydrophobicity, and molecular weight are utilized to search extensive compound databases for structurally similar molecules. Various techniques, including 2D and 3D molecular descriptors, pharmacophore modeling, and machine learning algorithms, facilitate the assessment of molecular similarity.
- Our company employs a sophisticated array of tools to conduct meticulous ligand-based virtual screening. By leveraging these cutting-edge resources, we enable the exploration of vast chemical space, accelerating the identification of promising drug leads.
Structure-Based Virtual Screening
Contrary to ligand-based screening, structure-based virtual screening revolves around the 3D structure of the target protein.
- The process commences with the acquisition of a 3D model of the target protein or a crystal structure, if available. Utilizing this structural information, a grid of potential binding sites within the protein is generated. Subsequently, a library of small molecules is docked into these binding sites, and their binding affinity is evaluated using computational scoring functions.
- Our company employs state-of-the-art techniques to exploit this structural information, aiding in the identification of potential ligands that complement the active site of the protein. With access to advanced computational tools, we facilitate structure-based virtual screening with unparalleled precision and efficiency.
ADMET Prediction
In addition to virtual screening methodologies, our company integrates ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction models to augment drug discovery efforts. These ADMET models predict crucial pharmacokinetic properties, offering insights into a compound's viability as a drug candidate.
- Our ADMET AI models encompass a comprehensive range of parameters, including solubility, permeability, plasma protein binding, metabolic stability, efflux, and hERG (human ether-a-go-go related gene) inhibition.
- Leveraging proprietary datasets and cutting-edge machine learning tools, our company's ADMET prediction models rival the performance of leading pharmaceutical companies.
By integrating these predictive models seamlessly into our virtual screening services, we empower researchers with invaluable insights at every stage of drug discovery.
