Bruton's tyrosine kinase (Btk), also known as agammaglobulinemia tyrosine kinase (TK), is a member of the Tec kinase family, and Btk has emerged as a novel target in medicinal chemistry. Btk is a cytoplasmic non-receptor TK that is expressed in all cells of the hematopoietic lineage, especially B cells, mast cells, and macrophages; in contrast, it is absent in T cells, NK cells, and plasma cells. This protein plays an important role in B cell lymphopoiesis and is important for the development, maturation, and differentiation of immature B cells, as well as for the proliferation and survival of B cells themselves.
Our Services
- Btk Target Analysis
Target structure information can be used in multiple aspects of drug discovery, such as target selection, hit identification and optimization, developing the final properties of compounds using multithreaded processes, and reconstructing biomacromolecular structures through rational design to optimize their draggability.
- Protein secondary structure prediction
- Homology modeling
- Molecular docking
- Molecular dynamics simulation
- Protein structural characterization
- PTM prediction
- QSAR Modeling and Drug Repurposing
Quantitative structure-activity relationship (QSAR) is a structure-based computational method through which a quantitative mathematical model can be established between chemical structure and biological activity. We apply multiple web-based tools and algorithms: QSAR-Co, FL-QSAR, Transformer-CNN and Chemception to provide QSAR modeling for our clients
- AI Drug Design
CD ComputaBio's AI-Design Platform, co-designed and developed by a computational science team and a top Internet company, can rapidly generate a collection of structurally novel compounds. The platform provides virtual screening, enabling rapid molecular evaluation, ranking, and candidate compounds.
- Candidate Nomination
In this phase we characterize the most promising leads from the lead optimization phase with the aim of producing a data package ready to submit for investigational new drug. Approaches we used include:
- Full characterization of compounds for promotion to development
- Focused on ADME around a specific dosage form
- Early toxicity studies including genotoxicity and carcinogenicity
- Draggability Assessment Using Machine Learning
Structure-based evaluation of target draggability involves predicting ligand-binding sites on proteins that are complementary to these drug-like properties. We use a machine learning approach to predict draggability and how it varies with the spatial and physicochemical properties of the binding pocket.
Our Capabilities
In R&D projects, we can offer our partners several core advantages:
- Mature and complete smart drug R&D platform: Our smart drug R&D platform is equipped with virtual R&D tools, and projects can be launched immediately.
- Super-large-scale computing resource support: Customers do not need to invest additionally in cloud computing. Based on the CD ComputaBio cloud architecture, they can rapidly mobilize computing resources of hundreds of thousands of cores to support research across multiple cloud provider platforms, thereby improving research and capital efficiency.
Our Strategies for Binding Energy Prediction of Btk and Candidates
Free energy perturbation calculation (FEP) is a method to evaluate the binding strength of small drug molecules and targets. We deeply combine FEP with the cloud computing platform to complete the high-precision evaluation of the affinity between candidate molecules and targets in batches, so as to effectively remove false positive molecules before entering into the experimental study of the binding activity of small molecules to the target.
* For Research Use Only.
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