Computer-Aided Drug Discovery

Ligand-based drug design can advance the drug discovery process at different stages by analyzing known ligands that bind to receptors in the absence of reliable target protein structure information. The specific methods include similarity search, establishment of pharmacophore model and QSAR model, skeleton transition, geometric optimization and conformational analysis, etc. In many cases, ligand-based drug design and structure-based drug design can be jointly applied to the development of drug candidates.

Compared with traditional methods, structure-based drug design is a more cost-effective and necessary tool for the lead discovery. It designs bioactive compounds based on the structure of the target or the structural information of known active compounds. Due to the recent volume and pace at which the 3-D structures of protein targets and their co-crystals have been made available, as well as the development of computing tools, structure-based drug design has become a tool for lead generation as well as for optimization.

Molecule docking has become one of the most useful drug design approach, in which the 3-D structure of a binding site can be analyzed and leads to a comprehensive investigation of the interactions between drug and receptor. Docking study plays an important role in the QSAR methods and homology modeling, so it is useful in the structure-based drug design.

Virtual screening, a commonly used computational technique in the field of drug discovery, is often applied to identify the most valuable structures, which can bind to a drug target, in a wide array of small molecules. BOC Sciences mainly focus on the designing and optimizing targeted combinatorial libraries. Additionally, our experts is able to enrich those libraries of available compounds from in-house compound repositories or vendor offerings.

ADMET properties refer to the absorption, distribution, metabolism, excretion and toxicity of molecules in organisms. ADMET prediction can help to exclude compounds with poor ADMET properties early so as to avoid costly structural modification in the later stage. At the same time, ADMET prediction can also evaluate the effect of structure optimization and evaluate whether the ADMET properties are indeed improved, so as to avoid excessive resources spent on synthesis.

Activity prediction is an important part in our CADD services, in order to predict the selectivity and affinity of compounds against targets. In this service, a molecule may be expanded to a wide range of conformers, and then their affinities to targets will be analyzed by our comprehensive evaluation system.

As an integral part of rational drug design, Quantitative Structure Activity Relationships (QSAR) has been developed to quantitatively analysis the relationships between the chemical structural-related properties and biological activity or target property) of compounds being studied. Moreover, QSAR is also recommended as a useful tool to determine the parameters responsible for biological response which have an essential effect on the optimization of lead compound.

Physicochemical properties are key factors in controlling the interactions of xenobiotics with living organisms. Computational approaches to toxicity prediction generally rely to a very large extent on the physicochemical properties of the query compounds. Consequently the reliable silico methods are important for the rapid calculation of physicochemical properties.