New drug design relies on a large number of targets, small molecules, and target-small molecule interactions in chemoinformatics and bioinformatics. Effectively select potential candidate compounds from a large number of small molecule compound can avoid blind activity screening of compounds, thereby reducing the labor, time and financial costs of discovering active lead compounds. The focus of small molecule compound library design is to create libraries based on numbers of properties simultaneously, such as diversity and drug-like physicochemical properties. Our well-designed libraries can produce HTS hits with properties that make them suitable for use in medicinal chemistry.
Figure 1. Screening strategy of compound libraries with small molecule microarrays. (Vehary, S. 2018)
Our Design Process
- Data storage and management
Various related data such as chemical parameters, spectral data, purity data, and biological activity measurement values of each compound are collected and stored in the database, and can be quickly recalled and operated.
- Structure-activity relationship research
Through the analysis of existing active compounds, a diversity of mathematical statistical methods (such as genetic algorithms, artificial neural networks, support vector machines and projection pursuit regression, etc.) are used to establish a structure-activity relationship model to measure the relationship between compound structure and biological activity relationship. At Alfa Chemistry, Hansch method, free-wilson method, molecular connectivity method, comparative molecular field method (CoMFA), and comparative molecular similarity method (CoMSIA) are available.
- Virtual database design
We can design database based on ligand structure and based on target structure.
- Data mining
Important information on substructure, two-dimensional/three-dimensional similarity, molecular shape, skeleton, and pharmacophore levels can be sorted out by analyzing existing compound data, which is useful in the selection of other compounds.
- Statistical analysis
Methods such as principal component analysis and factor analysis are used to more simply and effectively express molecular information and reduce the complexity of calculations.
- Visualization analysis
Visualization analysis is to automatically filter and express data by means of charts, and then we perform the analysis according to the generated results.
Our Small Molecule Compounds Libraries
- Biologically active compound library
Biologically active compounds are substances that can cause certain biological effects in the body and are the main source of small molecule drugs. We are able to design a biologically active compound library containing more than 8,700 small molecule compounds with known activities and clear targets.
- Drug library approved by Food and Drug Administration
Drugs and compounds approved by Food and Drug Administration that have passed clinical phase I have good biological activity, pharmacokinetic properties and safety, and therefore are particularly suitable for new use of old drugs, which can significantly accelerate the drug development process.
- Natural product library
Natural products are small molecule compounds produced from any organism, including primary metabolites and secondary metabolites.
- Drug-like diversity compound library
As an effective tool for scientific research and screening of new drug targets, the drug-like diversity compound library is an optimal choice for high-throughput screening (HTS), high-content screening (HCS) and computer virtual screening. Alfa Chemistry selects products with reliable quality and good drug-like diversity to establish a drug-like diversity compound library.
Features of Our Small Molecule Compounds Libraries
At Alfa Chemistry, our well-designed libraries have the following unique features:
- Compounds: 8.1 million small molecule active compounds, 1.3 million molecular skeletons, 57 K natural/marine products.
- Binding analysis: 9.3 million affinity test data, 10K targets, 330K physical and chemical properties.
- Function: 10.3 million function test data (in vivo test data and in vitro test data),
- ADMET: 1.6 million ADME and toxicity test data (29% absorption, 15% distribution, 25% metabolism, 4% excretion, 27% toxicity).
- Clinical development: pre-clinical data, clinical data and 5.6K listed drugs.
- Popular research fields: tumor, stem cells, nerves, epigenetics, etc.
- Signal pathways: TGFβ, mTOR, NF-κB, etc.
- protein targets: kinases, GCPRs, ion channels, etc.
What We Offer?
- 3D structure of the generated compound
- Biological activity data of organic small molecules
- Comprehensive drug target information
- Information on small molecular metabolites found in the human body including molecular structures of related proteins, genes and metabolites
- Interactions between drug target proteins and small drug-like molecules
- Interactive visualization database containing various small molecule pathways which can be used to support pathway elucidation, and pathway discovery in metabolomics, transcriptomics, proteomics and systems biology.
Alfa Chemistry's Advantages
- We provide custom libraries to help select diverse compounds from Alfa Chemistry and specify desired quantities.
- Alfa Chemistry' service team and scientifically trained personnel will be glad to help customers create a unique small melecule compound library to meet exact screening requirements.
- Our teams provide multi-level services and consulting suggestions on the establishment of small molecule libraries, warehousing, management and IT support, standard formats, etc.
Our small molecule compound library design services remarkably reduce the cost, promote further experiments, and accelerate the process of drug design for customers worldwide. Our personalized and all-around services will satisfy your innovative study demands. If you are interested in our services, please don't hesitate to contact us. We are glad to cooperate with you and witness your success!
Reference
- Vehary, S. Reactive Chemicals and Electrophilic Stress in Cancer: A Minireview. High-throughput. 2018, 7(2): 12.