Chemoinformatics is a relatively new principle of chemistry and is based upon the processing of data concerning chemical and molecular structures through the use of computational analysis.
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The analysis of these data allows the relationship between chemical structure, chemical properties, and molecular activity to be studied. It is an in silico technique, which means it is a form of scientific study which is performed virtually on a computer via software and simulations.
The normal process of drug discovery entails selecting a disease to target, then searching for potential compounds and molecules which can be used to reduce the severity of the disease in some way. This is done through many stages of screening, which normally compare the effectiveness of these potential molecules to stop a biochemical mechanism.
Chemoinformatics can drastically enhance this process, as one of the principal applications of chemoinformatics in research is the discovery and development of drugs. There are many techniques available in order to achieve this, and the use of software to calculate and visualize structures is crucial.
In order to reduce costs and speed up drug discovery when screening for new potential compounds that could be developed into drugs, virtual screening can be used to filter out certain compounds early on that aren’t compatible without the need for physical screening.
This method uses computer software to build virtual screens and simulations which can check for potential molecules that have the potential to be developed into drugs with much higher efficiency than conventional methods. Compounds are sorted and filtered by their solubility, their cross-reactivity with other compounds, and whether they contain potentially toxic groups.
High throughput screening
High throughput screening (HTS) is a classic technique used for drug discovery and development to test large numbers of different molecules for stimulatory or inhibitory effects in an automated screening process. It has become highly automated and very efficient in recent years, with liquids being used at a nanoliter scale, and advanced robotics used to carry out these screens precisely.
Chemoinformatics can be used for sequential HTS to more efficiently produce screening for ligand-receptor interactions. The stronger these interactions, the more viable the sample is for drug development.
Virtual screens are used to provide preliminary information for HTS. Virtually screening for compounds uses information from online databases of compounds, and software which calculates chemical interactions to reduce the costs and time, which normally come with the high throughput screening process. HTS is then carried out, producing more accurate results, which are compared to other online libraries to improve screening in the future.
In silico ADMET
The physical and chemical characteristics of a drug need to be fully understood in order to work out how exactly a drug molecule is absorbed by the body, distributed around the body, metabolized, and excreted. The toxicity of the drug in these circumstances is also vital knowledge. These categories of study are referred to as ADMET.
Normally all of these mechanisms are investigated after a compound has been identified for drug use, but using chemoinformatics, it is now possible to identify the characteristics of a drug for these mechanisms at a much earlier stage in development. Computational analysis is used to determine which compounds have good ADMET properties to use in the screening process.
This reduces the expanse of the screening process and therefore reduces the time needed for the discovery of drugs, costs, and difficulty. More drugs can be discovered and developed each year with the use of chemoinformatics.
Lo, Y. C., et al. (2018). Machine learning in chemoinformatics and drug discovery. Drug discovery today. https://doi.org/10.1016/j.drudis.2018.05.010
Umashankar, V., et al. (2009). Chemoinformatics and its Applications. General, Applied and Systems Toxicology. https://doi.org/10.1002/9780470744307.gat222
Xu, J., et al. (2002). Chemoinformatics and drug discovery. Molecules. https://doi.org/10.3390/70800566