Whether you need to crunch big data, formulate and test hypotheses, or simply decide which compound to make for your next iteration, computational sciences are invaluable. As computing power and the quality of algorithms continues to increase, so does the impact of computer-aided drug design (CADD) across all aspects of pharmaceutical development.
Why Choose our CADD Services?
Our computational drug designers apply their expertise in in-silico modeling, cheminformatics and machine learning to fast track your drug discovery journey to the clinic.
We offer our CADD expertise as a standalone service supporting your drug discovery activities, or fully integrated to our drug discovery offering. As an extension of your team, we advise on the best approach to overcome the scientific hurdles your project is facing. We help analyze your project data, formulate scientific hypotheses, and select the appropriate computational tool to test each hypothesis.
For specific work packages such as virtual screening, docking or script writing we also offer a Fee for Services model.
We provide full reports, models, and data in a range of electronic formats, according to your capabilities and access to software.
For more information about our in-silico drug discovery services, or to discuss how we can accelerate your programs, please contact us.
Drug Discovery Techniques
From classical modelling to cutting-edge machine learning, our CADD scientists have a proven track record in applying and developing in-silico technologies to enlighten pharmaceutical research.
Target assessment and modeling
Target identification and validation are arguably some of the most critical stages in drug discovery. Our scientists combine in-silico methodologies with in-vitro experimentation to evaluate the druggability of your biological targets. Our in-silico target assessment and modeling services include:
- Homology modeling: We build homology models when no suitable protein structure is available. Our models are rigorously refined, trained and validated by our experts. These are used for hit identification activities (e.g. virtual HTS) and to guide medicinal chemists during hit to lead and lead optimization
- Binding pocket assessment: We harness proven algorithms to assess the ligandability of your biological target by predicting and ranking binding sites (cryptic, transient, allosteric, etc.) likely to be involved in eliciting the desired biological response
- Selectivity analysis: We combine protein sequence analysis, structural modeling, and cheminformatics to enhance target selectivity.
Virtual high-throughput screening
Virtual high-throughput screening (virtual HTS) is a cost-efficient hit identification strategy involving the screening of virtual compound collections against ligand-based or structure-based in-silico models. Our computational scientists will design and execute the bespoke virtual HTS workflow that best suits your needs, typically including:
Virtual library selection: We have a curated virtual collection of over 30 million commercial screening compounds. Our computational scientists can also enumerate virtual compound collections tailored to your needs.
Establish virtual models: We utilize ligand-based or structure-based models. All our models are refined, trained and validated by our modeling experts to ensure fitness for purpose.
Virtual HTS: Along with the traditional virtual HTS technologies, we offer cutting-edge AI-driven virtual HTS . Underpinned by the MolPAL algorithm, this technology screens vast compounds collections using high-quality docking in the matter of hours. With hits rates of up 40%, this platform offers one of the leanest and successful hit identification strategies available on the market.
Our in-vitro assay and ADMET screening groups have decades of experience in designing bespoke and efficient screening cascades to confirm and prioritize hits in-vitro, sending your project on the fast-track to success.
In addition to virtual HTS, Concept Life Sciences’ medicinal and computational chemists have a proven track record in successfully implementing knowledge-based and fragment-based technologies for hit identification.
Structure-based drug design
Knowing the three-dimensional structures of target proteins can have a tremendous impact on your drug discovery process. When suitable structures are available, apply a structure-based drug-design approach, including:
- Binding site analysis
- Molecular docking (reversible, induced-fit, covalent, macrocyclic, etc.)
- Fragment elaboration from fragment screening hits
- PROTAC design for protein degradation
When a receptor structure is unavailable, we work with our X-ray crystallography partners to generate one. We also have extensive experience in building homology models from related protein structures and applying ligand-based drug design strategies that maximize SAR data.
Ligand-based drug design
No target structure available? Not a problem! Our scientists will select and apply ligand-based strategies to help you identify high-quality scaffolds for your projects.
- Scaffold hopping and bioisostere scouting: Whether you need to gain intellectual freedom to operate, or eliminate liabilities associated with your chemical series, our scientists can help. We can enumerate >10,000 scaffold hop opportunities, and individually evaluate and prioritize each scaffold hop
- QSAR: We employ field-based QSAR modeling to define rules predicting the activity of novel compounds
- Pharmacophore modeling: We exploit ligand SAR to build lean models defining the spatial arrangement of chemical features that drive receptor recognition. These models not only enable virtual HTS in the absence of receptor structures, but also help identify novel biologically-active chemotypes
Our medicinal chemistry team has a proven track record of designing and implementing synthetic routes to the most challenging compounds, thus unlocking novel IP spaces.
Successful lead optimization is a resource-intensive process that aims to identify potent, selective, efficacious and safe compounds suitable for preclinical evaluation.
We strive to run efficient lead optimization campaigns by taking the utmost care in prioritizing the best target ideas. At this stage, each new target we design and synthesize aims to test a specific hypothesis or address drug discovery liabilities. This is often enabled by advanced ab initio quantum mechanical (QM) calculations, including:
- QM pKa determination
- Conformational/rotational energetics
- Tautomeric equilibria
- H-bond strength
- ADME/Tox profiling
- Free energy perturbation
By integrating advanced computational modeling, our drug discovery experts will make giant scientific leaps for your projects, saving you months of unnecessary research work.
Our computational scientists employ a range of cheminformatics techniques to support all aspects of drug discovery.
New ideas are generated in-silico (e.g. library enumeration, hit expansion design, 2D- or 3D-similarity searches) and refined by computational and medicinal chemists.
Virtual profiling of these ideas is routinely carried out via the computation of CNS-MPO scores (for CNS indications), pKa and ADMET predictors, and many more.
Optimization of hit and lead series are accelerated by bespoke Sparse Array design, and enabled by our parallel chemistry platform.
High volumes of data can be exploited by our experts through molecular matched pair analysis (MMP analysis) and clustering.
We also take pride in constantly aiming for the highest level of data integrity. All our experiments are recorded using our internal electronic laboratory notebook (ELN) systems. We can even work on your ELN platform for full integration within your own systems.