Computational Simulations Lead to Accelerated Drug Design
In silico study in medicine takes advantage of computer simulations to predict the protein-ligand binding site, reducing real laboratory experiments and accelerating the drug discovery process in a more efficient and economical way. At BOC Sciences, we focus on pharmaceutically interesting proteins that could become targets for the next generation of pharmaceuticals. Working closely with our in-house multi-disciplinary scientists in chemistry, biology and pharmaceutical sciences, we have strong capabilities to accelerate the novel template design and library compounds screening.
Development of new algorithms, for the detection of structurally similar protein binding sites, enables us to search for local similarities in physicochemical properties in different protein surface structures independently of sequence or fold. Adapting specialized computational hardware and super high-performance computer cluster with total 60 blades and 720 cores, our system is capable to evaluate >1,000,000 compounds in less than 10 days.
Our scientific team has established virtual compound database for different client-based projects with more than 2 million unique compounds, including ZINC, MDDR, ACD, NCI, etc. Taking advantages of QSAR analysis, Similarity Search and Scaffold Hopping for Structure Optimization, we provide virtual screening services on molecular docking, molecular dynamics simulations, free energy calculations and pharmacophore modeling. We have accomplished several cutting edge industrial projects that involved a lot of programming. At BOC Sciences, our expertise offers the best services in the combination of computing and biochemistry.
Advantages of Our Services:
The general scheme of a Structure-Based Virtual Screening (SBVS) strategy starts with processing the 3D target structural information of pharmaceutical protein interested. The target structure can be derived from experimental data (X-ray, NMR or neutron scattering spectroscopy), homology modeling, or from Molecular Dynamics (MD) simulations. The identification of ligand binding sites on biological targets is incredibly important. Our expertise evaluates the druggability of the receptor, the choice of binding site, the selection of the most relevant protein structure, incorporating receptor flexibility, suitable assignment of protonation states, and consideration of water molecules in a binding site. Our highly experienced drug design professionals carefully choose the compound library to be screened in the virtual screening (VS) exercise according to the target in question, and preprocess libraries to assign the proper stereochemistry, tautomeric, and protonation states.
In the absence of three-dimensional (3D) structures of potential drug targets, ligand-based drug design is one of the most popular approaches for drug discovery and lead optimization. Pharmacophore modelling, an ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target and to trigger (or block) its biological response, is a widely used tool in ligand-based drug design and can provide predictive models suitable for lead compound optimization. At BOC Sciences, we generate a pharmacophore model by superposing a set of active molecules that are assumed to bind to the same target with the same binding mode, and extracting common chemical features that are essential for their bioactivity. Such a model can then be used to virtually screen libraries of compounds with the aim of identifying potential new binders of the target of interest. At BOC Sciences, we offer ligand-based virtual screening (LBVS) services include similarity and substructure searching, quantitative structure-activity relationships (QSAR), pharmacophore mapping, and machine learning.
BOC Sciences aims at developing the most accurate in silico methods to overcome bottlenecks in drug discovery and design innovative medicines to treat important disease. Utilizing the tool of docking studies, we have strong capabilities to prioritization novel templates, minimizing the real lab-based experimental costs and driving the discovery process in a much more efficient and economical way.