Saying that computational methods are a very common resource for chemistry researchers would be an understatement. Computers have become so powerful that anyone interested in researching a wide range of chemistry topics will surely resort to them for analysis, calculations, estimations, and predictions. 

The introduction of new technologies and approaches like machine learning and data science has only deepened this dependence for chemistry researchers. In fact, the field of cheminformatics (that is, solving chemical problems with computers) has only grown since it was defined as such back in 1998. Today, tackling any kind of research within the chemistry field without the intervention of computers seems highly unlikely.

Cheminformatics, defined

A broad definition of cheminformatics would be the combination of computational resources to solve practical questions. The man responsible for coining the term, Frank K. Brown, would add that, by using computers, researchers are transforming “data into information and information into knowledge for the intended purposes of making better decisions.”

With such a broad way of understanding it, it’s not surprising to learn that cheminformatics is widely popular, especially in the pharmaceutical industry. Pharmaceutical companies have long used computers for the so-called drug design, looking for specific compounds and taking deeper looks at molecules looking for specific biological properties and effects. 

However, cheminformatics is extremely popular beyond that industry. Biotechnological researchers have also found that computational power can bring a lot of benefits to their tables. Thus, they are finding that modern devices can help in the development of novel biotechnological processes and products to aid their researches.

Why is cheminformatics important

For anyone looking in from the outside, chemistry research might feel like something from outer space. That’s because doing so requires complex processes, abstraction, deep knowledge, and tons of unstructured and structured data. So, it’s only natural for some people to ignore how incredibly important chemistry research is for our everyday life. And in that context, cheminformatics plays a huge role.

Here are some of the main applications and what they mean:

  • Storage and retrieval of information: probably the main application of cheminformatics. Storing, indexing, and searching the vast amount of information available from molecules and compounds could be an exhausting task. Thankfully, advances in computer science, especially in data mining and artificial intelligence, has made it possible for chemists to access a wide database of 2D and 3D representations containing highly-detailed records of past work. This is important for research as the increase in information availability makes it easier for researchers to make more informed decisions while working on the lab.
  • Virtual libraries: the pharmaceutical industry has been using the power of cheminformatics for the development of new drugs. This wouldn’t be possible without the existence of virtual libraries. By using data coming from real and virtual molecules, researchers can generate virtual libraries of compounds that let them explore chemical environments and theorize the creation of new compounds with a certain set of properties. By using real classes of compounds and using sophisticated machine-learning-based algorithms, it’s possible to generate these new compounds that are similar to the real ones. That can later serve as a basis for its real creation and the final development of new drugs based on them.
  • Virtual screening: this is a computational technique that searches libraries of molecules to identify structures that have high chances of displaying a biological activity against a certain target. In other words, computers are used to screen through vast databases to search for specific molecules that have certain properties that make them candidates for interaction with a defined target. This means that virtual screening is a fantastic aid in finding compounds that can act in treatments against a variety of diseases. And since virtual screening is done in the early stages of research, it saves time and money on costly research.
  • Quantitative structure-activity relationship (QSAR): the ability to predict how a specific compound could act based on its individual analysis allows for a lot of cost and time savings. That’s why QSAR analysis is so important. By using chemical expert systems, researchers are capable of estimating the physiochemical properties and biological activity of chemical molecules. The information provided by this analysis is invaluable and goes beyond savings since it can lead to a body of knowledge that can further new hypotheses and inform new decisions across a variety of scenarios.

The challenges ahead

Though most tools used in cheminformatics are complex and advanced, it’s imperative for researchers to keep working with developers and QA testing services to ensure that digital platforms maintain their accuracy and respond to the questions posed by the research themselves. In that way, there are two major challenges for the field of cheminformatics.

The first and most important one is a conceptual challenge – to come up with refined technologies capable of extracting knowledge from large-scale raw databases in short periods of time. Today’s computational methods in chemistry (such as molecular dynamics, statistical machine learning, and quantum mechanical methods) aren’t suited for intensive chemical research, as they can’t be scaled to large datasets (not with the same performance they display in small datasets, at least).

The second challenge is more closely related to the software development world. Artificial intelligence-based solutions can be a great answer to tackle the challenge above, especially deep networks. But for them to perform quick and efficiently, they have to be further developed, tested, and trained. As the understanding of these new technologies grows, so will their specific applications, of which cheminformatics is just one of them.

All in all, chemoinformatics has a challenging yet bright future. Taking a look back to the relationship between computers and chemical research is easy to see how far the discipline has come. This means that the foundations are laid down for further development, as chemical researchers work with software developers side by side to augment the computational capabilities when applied to various issues stemming from the chemical field.

LEAVE A REPLY

Please enter your comment!
Please enter your name here