BOC Sciences' Computer-Aided Drug Discovery (CADD) team can help you accelerate drug development and clinical development by designing novel compounds to provide research projects and develop advanced solutions for researchers around the world. Our team has a wealth of experience in informatics, virtual screening and molecular modeling to provide customers with a full range of computer-aided drug design support and services.
Computer-aided drug design (CADD) is a widely used technology using computational tools and resources for the storage, management, analysis and modeling of compounds. It relies on digital repositories for the study of designing compounds with physicochemical characteristics, predicting whether a given molecule will be combined with the target, and if so how strongly. Computer-based methods can help us to search new hits in drug discovery, screening many irrelevant compounds at the same time and study the structure-activity relationship of drug molecules.
The advantages of CADD
The traditional strategies for discovering new drug usually begin with taking a lead structure, and then develop a chemical program to find an analog molecule which can exhibit the desired biological properties. The process often involved a long cycle and a large number of experiments, in which medical chemist utilizing their experience and intuition to ultimately select a candidate for further development. The whole process is laborious, expensive and inelegant. Contrary to traditional drug design methods, CADD has been applied as a highly effective tool for systematic assessment of potential lead candidates before they are synthesized and tested. In this way, it saves time as well as cost in drug discovery.
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.
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.
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