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Research project in any scientific field is a complex multi-step process. Early-stage Drug Discovery is not an exception. There are lots of questions to be answered before even thinking of getting started. These might probably be the most frequent ones:
- How do I select a target?
- Which approach is the best suitable for my target?
- Who can help me with multiple approaches?
- Where can I purchase the hit molecules?
There are hundreds of companies that are world renown experts in high-quality products and services. Knocking on every door, evaluating, and comparing is close to impossible in terms of time management. So, is there a partner, just one point of contact who has everything in one place and could help me? I have great news for you — the answer is «yes» — Chemspace! We are making researchers’ lives way more simple and easy because we:
1. Provide access to the largest catalog of both in-stock and make-on-demand molecules (building blocks, intermediates, reagents, consumables, screening compounds, fragments, peptides, proteins, antibodies, and biological kits);
2. Support navigation in ultra-large chemical spaces (Freedom Space, Enamine REAL);
3. Cover all stages of the procurement: from ordering products to delivering to your door;
4. Moreover, we provide comprehensive Discovery Services, such as computational chemistry, molecular docking, virtual screening, and machine learning, and QSAR to name a few. Let us have a quick overview of each of them:
Computational chemistry is one of the most innovative branches that is widely used in addressing complex Drug Discovery challenges. This approach uses programs that can analyze giga-scale chemical databases and catalogs. By creating different simulations, computational chemistry shows opportunities in wide spectra of chemical interactions being simultaneously effort and time-saving technique. Computational chemistry helps to answer the questions on activity and affinity, as well as finding analogs to hit molecules.
Molecular docking. It is an efficient method for protein-ligand interaction analysis and screening of large compound libraries. This approach predicts the binding mode and binding affinity of a molecule to a protein target. Information received further can be used in the selection of the drug candidate and in minimizing off-target effects.
Virtual screening (VS). It is the most common approach in modern Drug Discovery. VS comes into play when you are dealing with a huge database. It allows researchers to analyze compound libraries and identify structures that are promising for your drug target. This method shortens the cycle of new drug development and reduces direct research costs.
Machine learning (ML) is an extremely hot topic now, isn’t it? It is increasingly being used in chemistry to accelerate the Drug Discovery process. ML has a considerable number of applications: analyzing dataspaces of chemical compounds and identifying drug candidates, predicting chemical properties, and optimizing experimental processes. Overall, machine learning represents a potentially great way to speed up the exploration of chemical compound space.
Few words on Quantitative Structure-Activity Relationship (QSAR). This computational method predicts physicochemical properties of compounds based on their structures. The main measurable physical descriptors for QSAR are hydrophobicity, polarity, and steric hindrance, which are known to affect the activity of bioactive molecules.
We at Chemspace provide you with all the above-mentioned approaches and techniques. Working with us you are having just a one-stop-shop solution. We will make sure to bring your research project to a new level!