What is chemoinformatics and how does it help discover new drugs?

Did you know that discovering a new drug takes between 10 and 15 years and costs billions of dollars? The amount of time, money, and effort invested is enormous, but that's all changing thanks to a scientific discipline known as chemoinformatics.What it is and how it helps discover new drugsThe answer is as exciting as it is complex, and in this post we'll explain it in a simple way.

What is cheminformatics? The exciting fusion of chemistry and computer science

To understand What is cheminformatics?Imagine you have to find a unique key that opens an extremely complex lock. But the key is hidden among a mountain of ten billion different keys. What a task! Can you imagine how much time and effort it would take to manually search and try each key one by one?

Well, the pharmaceutical industry faces this monumental challenge. The lock represents a disease-causing protein, and the key is a chemical molecule that could be converted into a drug. For decades, Experts have used 'manual' systems to find each new drug, investing a truly enormous amount of time, money and effort.

Returning to the analogy, imagine that you now have a intelligent system It's able to immediately rule out nine out of ten keys that don't fit. The system also helps you predict which keys have the most promising shape, gather them, and sort them into bunches. Great! That's, in essence, the magic of Cheminformatics.

What is cheminformatics? According to the portal PubMed, 'is a field of information technology that focuses on the collection, storage, analysis, and manipulation of chemical data.' This scientific discipline uses computer science and data science techniques to solve complex problems in chemistryIt is primarily focused on drug discovery, but also has applications in multiple sectors (agrochemicals, food, etc.).

Two fundamental pillars: Data and Algorithms

To understand how cheminformatics works, we must talk about its two essential components: chemical data, on the one hand, and the algorithms and models, on the other hand. The latter are used to process chemical data and thus obtain useful information that allows for the optimization of drug development. To do this, it is first necessary to digitalize all the data related to each existing chemical compound.

So it all starts with the digitalization of moleculesThese can be represented digitally using special formats (such as SMILES, InChI, or SDF files) that a computer can understand and process. Of course, we're not talking about simple drawings: these files encode information such as atoms, their bonds, their three-dimensional structure, electrical charge, physical properties, etc. This has resulted in the existence of gigantic databases storing millions of molecules, both natural and synthetic.

• Once the chemical compounds, with all their characteristics, are brought to the digital plane, it is possible to apply computational tools to them.
• This is what cheminformatics is about: applying chemical data statistics, the automatic learning, artificial intelligence, data mining and pattern recognition methods.
• All of these algorithms and models greatly speed up the analysis of such a huge amount of data, with the ultimate goal of developing drugs.

How cheminformatics helps discover new drugs

Basically, what cheminformatics does is optimize every stage of the drug discovery and development processIt's worth noting that this process is a long and complex cycle that can take 10 to 15 years and cost billions of dollars. But much of this effort has been greatly simplified thanks to the fusion of chemistry and computer science. Let's look at how this is possible during the early stages of drug development:

Stage 1: Discovery and Research

To create a drug, the first thing scientists do is investigate what causes a disease. Within that cause, They identify a biological target or objective (such as a protein or gene) that can be altered to treat the disease.. At this point, cheminformatics helps to know if a target is "druggable", that is, if it has a bolt (returning to the initial analogy) in which to introduce a key (molecule) to try to modify it.

In addition, data processing techniques also help to identify and create candidate molecules (bunches of keys) that could interact with the target. Instead of physically testing millions of compounds, a virtual screening in massive databases to identify the best candidates. Thus, what used to take two to four years is now accomplished in much less time and with a smaller investment of money and effort.

Stage 2: Preclinical phase

In the preclinical phase, the most promising compounds identified are taken and rigorously studied to evaluate their safety and efficacy. These studies are typically conducted both vitro (on cells and tissues) as in vivo (in animals). But, Chemoinformatics allows all these studies to be simulated in silico, that is, on a computer, and with results very similar to laboratory tests. Naturally, this saves resources and time, and avoids synthesizing hundreds of useless variants.

Stage 3: Clinical trial phases

If preclinical studies are successful, the compound moves on to human testing. Of course, such a compound may be very potent in a test tube or in a digital simulation. But if the human body doesn't absorb it, it's toxic, or the liver metabolizes it too quickly, it will be a drug failure. Therefore, before testing in humans, it's necessary to conduct a ADMET Properties Prediction Test, which measures Adsorption, Distribution, Metabolism, Excretion and Toxicity of the compound in the human body.

Fortunately, Cheminformatics models can also run ADMET property prediction testsThis can be done even before testing the compound in animals, in order to rule out problematic candidates early on. Again, performing these digital simulations reduces the number of failed clinical trials, as well as the need to use test subjects (and the resulting ethical impact).

In conclusion, we have seen in broad strokes what chemoinformatics is and how it helps discover new drugs. The scalability of this scientific discipline is enormous., so more and better results are expected in the future. By combining the power of chemistry with computational intelligence, a whole universe of possibilities opens up for treating diseases more quickly, accurately, and economically.