Category: Cheminformatics & Drug Discovery Certification Course

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.

Chemistry is largely built on experimental observations and data, it deals with compounds, their properties, and their transformations. Compounds and chemical reactions are the static and dynamic aspects of chemistry. The entire living and material world consists of compounds and mixtures of compounds. Compounds are transformed into each other by chemical reactions that can be run under a variety of conditions.

Although the laws of chemistry are too complicated to be solved, chemists still can do their jobs and make compounds with beautiful properties that society needs and chemists still run reactions from small‐scale laboratory experiments to large scale reactors in the chemical industry. The secret to success has been to learn from data and from experiments. The process of learning is called inductive learning as shown in Fig. 1.

The amount of data and information is enormous and increasing rapidly. At present, more than 41  million different compounds are known, all have a series of properties, physical, chemical and biological; all can be made in many different ways, by a wide range of reactions; all can be characterized by a host of spectra. Problem is to extract knowledge from these data and use it  to make predictions.  Three major tasks of structure‐property / activity relationships, design of reaction/syntheses and structure elucidation are tackled by making use of prior information,  and of information that has been condensed into knowledge. The amount of information that has to be processed is often quite large.

This immense amount of information can be processed only by electronic means, by the power of the computer. This is how cheminformatics is useful.

What is Cheninformatics?

Cheminformatics is the study of all aspects of the representation and use of chemical and related biological information on computers. It has applications in drug discovery health, data mining, and many other areas. According to the National Center of Biotechnology Information, cheminformatics is a relatively new field of information technology that focuses on the collection, storage, analysis, and manipulation of chemical data. The chemical data of interest typically include information on properties, spectra, small molecule formulas, structures, and activity (biological or industrial).

Cheminformatics originally emerged as a channel to assist the discovery and development process of drugs. However, cheminformatics now plays a crucial role in many areas of chemistry, biochemistry, biology. Since the 1950s, several foundational algorithms of cheminformatics have been described. But open-source software, implementing the algorithms, became accessible only since the mid-1990s, which is around 40 years later. But in 2004, a large public small molecule structure repository was made freely available by the National Library of Medicine.

History

There is no particular point in time that determines when chemoinformatics was founded or established. It slowly evolved from several, often quite humble beginnings. Scientists in various fields of chemistry struggled with the development of computational methods, which allowed them to manage the enormous amount of chemical information and to find relationships between the structure and properties of a compound. During the 1960s some early developments appeared that led to a flurry of activities in the 1970s.

Importance & Scope of Cheminformatics: 

• Cheminformatics is the use of computer and informational techniques applied to a range of problems in the field of chemistry. These information techniques to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and optimization.
• Cheminformatics combines the scientific working fields of chemistry, computer science, and information science in the areas of topology, chemical graph theory, information retrieval and data mining in the chemical space.
• The primary application of cheminformatics is in the storage, indexing, and search of information relating to compounds.
• Cheminformatics is a discipline organizing and coordinating the application of computers in chemistry.
•  Cheminformatics covers complementary disciplines that hold great promise for the advancement of research and development in biological systems, software development, techniques, drug design, and so on.

NEED OF CHEMINFORMATICS

The primary application of chemoinformatics includes storage, indexing, searching for information about the appropriate compounds. It maintains an access amount of chemical data and also accesses it by using a proper database. It is a significant application of information used to organize, analyze, to solve other new problems and to understand scientific data in the development of novel compounds.