Computer-Aided Drug Design (CADD)
- ADMET Modeling and Prediction
- De Novo Drug Design
- Ligand Based Virtual Screening
- Quantum Mechanics for Target Selection
- Structure-Based Virtual Screening
- DNA-Encoded Library Technology (DELT)
- Fragment-Based Screening
- High Content Screening (HCS)
High Throughput Screening (HTS)
- Automated HTS Platform
- Biochemical assays in Hit Characterization
Biophysical Assays in Hit Characterization
- BLI for Affinity-based Hit Screening
- CD Spectrometry for Protein Structure Determination
- ITC for Binding Assessment
- MS for Structure Confirmation
- MT for Binding Affinity Measurement
- NMR Spectrometry for Tareget identification and Characterization
- SPR Spectrometrys for Structure Determination
- TSA for Protein's Stability Evaluation
- Cellular assays in Hit Characterization
- Drug Repurposing
- Hit Screening
- HTS Assay Development
- HTS Compounds Libraries
- HTS Data Management
- Virtual Screening (VS)
- Experienced and qualified scientists functioning as project managers or study director
- Independent quality unit assuring regulatory compliance
- Methods validated per ICH GLP/GMP guidelines
- Rigorous sample tracking and handling procedures to prevent mistakes
- Controlled laboratory environment to prevent a whole new level of success
BKD for Taget IdentificationINQUIRY
As an another machine-learning approach, Binary Kernel Discrimination (BKD) is a powerful tool to identify the potential active compounds. It uses a training set of compounds whose structural and qualitative activity data are available to generate a model that can be further applied to rank other compounds based on their possible activity. In BKD, the kernel function is used to computationally calculate the similarity between a test set molecule and the members of the training set.
The Process of BKD And Related Operations
In the first section, the molecule is represented as 2D fragment bit-string.
We perform similarity searching using different coefficients.
We have different weighting schemes for lead compounds.
- Our BKD services are applicable to a very large file of 2D fingerprints as well as different pharmaceutical database.
- We can analyze the dis-similarities between a single test set compound and the training set compounds using the ranking results.
- Our teams deliver a final score which predicts the likelihood of that test set compound being active, assisting in selecting and identifying optimal active compounds.
- We draw a reliable prediction conclusion from the BKD method which enables to provide data support to our downstream services including similarity searching and structural analysis.
- At BOC Sciences, we have rich experience in the operation of binary kernel discrimination and we can customize the virtual screening strategy according to your specific demand.
- Ghafary, B.; et al. A Computer Aided Detection System for Cerebral Microbleeds in Brain MRI. Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro.IEEE International Symposium on Biomedical Imaging. 2012.
※ It should be noted that our service is only used for research.