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Chemoinformatics

chemoinformatics
  • 2D QSAR and 3D QSAR approaches
  • Docking and virtual screening
  • QM/MM calculations
  • Chemical compounds similarity searching and scoring
  • Chemical databases hierarchical clustering, K–means or K–nearest neighbours clustering
  • Structural alignment of proteins, ligands alignment, protein homology based modeling, studies of proteins and ligands conformational ensembles
  • Virtual chemical spaces design, clustering, analysis and usage for virtual screening
  • Library profiling, comparison and analysis
  • Molecular dynamics of proteins and protein-ligand complexes, including coarsegrained molecular dynamics, high throughput molecular dynamics and molecular dynamics with thermodynamical integration
  • Other – just send us a request

Bioinformatics and Data analysis

Medicinal Chemistry
  • OMICS data analysis and integration: Exome & Genome, Transcriptome, MicroRNA, Metabolomics, Epigenomics, SNP ,GWAS and EWAS, Proteomics, Metagenomics, Hi-C (3C, 4C, 5C) data analysis
  • Systems biology: Network analysis, pathways analysis, data analysis and integration
  • Statistical data analysis, big data processing, big data mining
  • Machine learning methods (regression, K-nearest neighbors, Support Vector Machines) and deep learning (restricted Boltzmann machines, deep belief networks, convolutional neural networks)

Medicinal Chemistry

Medicinal Chemistry
  • Synthesis–wise structural optimization of compounds for potency and ADME / Tox improvement
  • Systematic structure–activity relationships studies and optimization
  • Hit2Lead development, lead optimization, final leads selection and candidate selection
  • Synthesis–wise lead optimization
  • ADME/ Tox properties optimization
  • DMPK properties optimization
  • Multi–dimensional properties optimization (potency, synthetic accessibility, selectivity, ADME/Tox profile, patent purity, removal of the off–targets activities) and trade–offs minimization
  • Diversity of the areas of expertise (e.g. oncology, inflammation, neurogenic diseases, antiviral, antibiotics etc.) and biotarget types (GPCR, kinases, other enzymes like Zn–containing enzymes, transcription factors, etc.)
  • Other – just send us a request

Organic Synthesis

Organic Synthesis 2
  • Custom Organic Synthesis
  • Natural Product Synthesis and modifications
  • Peptide and peptidomimetic synthesis
  • Synthetic Pathway Design
  • Optimization Synthetic Scale up Work & API production
  • Retrosynthetic analysis
  • Other – just send us a request


Biological studies
  • In vitro biological assays
  • Colorimetry
  • Flourimetry
  • In vivo studies
  • Different animal models studies (mice, rat, rabbit and bigger).
  • Other – just send us a request

Analytical chemistry and proteomics
Analytical chemistry and proteomics
  • Mass spectrometry (proteins and chemical compounds)
  • IR, UV
  • NMR (chemical compounds, proteins in solution)
  • Other – just send us a request


Patent and IP services
Patent and IP services
  • Patent purity check
  • Patent search
  • Patent writing
  • Consolidation of the IP position
  • Other – just send us a request


IT and programming
IT and programming
  • Server management, Linux/Mac OS/Windows
  • C/C++, PHP, Perl, Java CDK, TCL/TK, Python, Data analysis (R, SAS).
  • Coding for parallel MPUs architecture, GPU (CUDA Nvidia)
  • Other – just send us a request

Biomedical Animation
  • Autodesk Maya, Unity animation
  • Molecular dynamics animation
  • Proteins animation
  • Biomedical illustrations
  • Other – just send us a request
  • Case Examples
    Case Examples
    Case 1
    During high–throughput screening you have identified series of biologically active compounds against the desirable target. These compounds are called primary hits.
    Step 1: Confirmatory assays
    We can start from this point. In order to confirm that biological effect is real, that desired bio–target is affected, that there is no unexpected toxicity or other side–effects (like specific reaction on high–throughput screening components, not related to biotarget) we need to perform a number of confirmatory assays. Hit confirmation can also involve deconvolution of the biotarget, using iTRAQ.
    Step 2: Hits prioritization, scaffold selection
    After the hits are confirmed, they should be prioritized according to perspective of becoming a lead compound. Hits should be analyzed in terms of chemotype synthetic accessibility, potency, Lipinsky and drug–lead like parameters. For example compounds with smaller Mw, higher LogP, lower PSA and higher potency will be more desirable for the start in case of good synthetic accessibility. After hits are prioritized, the best chemotype and scaffold for further development is selected. It can correspond to several compounds. Then we use Computer–assisted drug design methods in order to find closest analogues with desired properties for each of the hits, spending most of the resources for top ranked hits. Then we prioritize initial hits list again and select from one to several chemotypes those, which analogues which will be studied further.
    Step 3: Hit2lead development
    After selection of the chemotypes, we start Structure–Activity relationships work. This involves the work of experienced medicinal chemists teamed with computer chemists (QSAR, Drug design), who take hit compounds from selected chemotypes and suggest their analogs. Ordinarily this starts from analogues from commercial chemical libraries and then goes into the synthesis rounds. The most potent compounds, having combination of the best potency, synthetic accessibility, pharmacokinetics & toxicity perspectives will be selected as leads for the further optimization.
    Step 4: Lead optimization

    Lead optimization may involve:

    • QSAR approaches
    • Structure–based drug design, which will help us to understand better the importance of each particular pharmacophore group for biological activity of the lead. It can be used for de novo design of the more potent compounds and scaffold hopping in combination with other parameters optimization, like ADME/Tox properties.
    • Biological assays like biochemical and cellular assays to confirm biological activity of optimized leads
    • Biological assays like biochemical and cellular assays to confirm biological activity of optimized leads
    • After lead optimization, we get a compound, ready for preclinical animal trials and clinical trials candidate selection.
    • Non–standard cases examples
    • Multitargeting compound, which may need to target more than one target
    • Covalentbinding compound, which needs to covalently bind the biotarget
    • Other – just send us a request

    News & Publications
    Multi-omics approaches to disease
    September 16, 2017 – 17:28
    High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of “integrative genetics”. Other omics technologies, such as proteomics and metabolomics, are now often incorporated into the everyday methodology of biological researchers. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies disease.

    Entering the ‘big data’ era in medicinal chemistry
    May 15, 2017 – 15:09
    Medicinal chemistry is experiencing the advent of the big data era, which biology already entered more than a decade ago, due to the availability of high-throughput genomics technologies. In medicinal chemistry, which is an integral part of drug discovery and traditionally a conservative scientific discipline, big data primarily comprise rapidly increasing numbers of compounds and volumes of associated activity data.
    While the practice of medicinal chemistry is just beginning to experience big data phenomena, it is evident that big data will play an increasingly important role going forward. More awareness of big data issues and potential caveats will still need to be raised to positively impact the field.

    
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