The normal process of drug discovery involves searching for potential molecules and compounds that can be used to minimize the severity of a particular disease. Several stages of screening are done to compare the effectiveness of these potential molecules to stop a biochemical mechanism.
This process can be drastically accelerated with the help of cheminformatics, whose principal application in research is the discovery and development of drugs. There are many techniques, software, and resources available to achieve this. Let’s look at some of the applications of cheminformatics in drug discovery.
- Databases of relevant compounds and metadata
Chemical databases of compounds and molecules relevant to drug discoveries are the backbones of computer-aided drug discovery, whether it is chemoinformatics or bioinformatics. These databases provide information that can be utilized to build knowledge-based models for designing and discovering drug molecules. Most popular databases include PubChem, ChEMBL, ZINC, and NCI.
- 2D virtual screening
Virtual screening (VS) is a computational technique used in drug discovery to search libraries of small molecules so that those structures which are most likely to bind to a drug target, typically an enzyme or protein receptor can be identified. In 2D virtual screening, databases of chemical structures are interrogated to find compounds that are similar to known actives (similarity searching).
- 3D virtual screening
In 3D virtual screening, databases of chemical structures are interrogated to find compounds that possess a pharmacophore or substructure in common with a known active (pharmacophore and substructure searching).
- Property prediction
Predicting properties like solubility and logP is very important in pharmacology and drug discovery.
- ADMET prediction
It is important to understand the chemical and physical characteristics of drug molecules in order to predict how exactly a drug molecule is absorbed by the body, distributed around the body, metabolized, and excreted. It is vital to know the toxicity of the drug in these circumstances. Such studies are referred to as ADMET.
- Clustering
Clustering of chemical compounds, otherwise known as unsupervised machine learning is used in drug discovery, mostly in preliminary analyses of large data sets of medium and high dimensionality as a method of selection, diversity analysis, and data reduction. Compared to the other costs of drug discovery, clustering can add significant value at a minimal cost.
- Quantitative structure-activity relationship modeling
Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery as it predicts how functionalizing the compound relative to a chemical series can increase its potency and other properties.
- Modeling or predicting protein-ligand binding
Identifying possible ligand-enzyme interactions is of major importance in many drug discovery processes as enzymes are one of the most important groups of drug targets. There are computational methods that can apply the information from resolved and available ligand-enzyme complexes to model new unknown interactions and therefore contribute to answering open questions in the field of drug discovery like the identification of off-target binding, unknown protein functions, induced-fit simulations, and ligand 3D homology modeling.
- De novo design
It involves designing molecules from scratch. De novo drug design is an iterative process in which the 3D structure of the receptor is used to design novel drug molecules. novo designing, the structure of the lead target complexes are determined and lead modifications are designed using molecular modeling tools.
Cheminformatics is a very useful field of science that highly contributes to science. It is extremely helpful in designing and discovering drugs to avoid the try and error method, which might be very costly. Cheminformatics also made it easy for scientists to search for molecules, their structures, and detailed information using the numerous databases available. Therefore, a focused and in-depth study in cheminformatics could help scientists and professionals in developing new drugs for many diseases that are still a threat to humanity.