It was not long ago when Artificial Intelligence (AI) first captured people’s imagination. In 2011, IBM first launched its supercomputer, and from then on, it was no turning back. It would have been difficult for people to imagine something SIRI and Amazon Alexa to become a commonplace phenomenon at the time, but the possibilities were limitless unlike today when constant innovations bring revolutionary transformation each passing of the day.
Since the first introduction of Artificial Intelligence (AI) to the world, the field has evolved greatly, diversified, and thus become vast. However, the one that has found many applications in the day-to-day life is Machine Learning that already leaves a bigger impact in current context. Machine Learning is about training the computer to make observations from the inputs. For instance, an image of a dog would be used as an input, and the computer will observe the information that it is a dog. This way, every time the computer comes across an image of a dog, it would have learned to recognize it.
There are many such ways that Artificial Intelligence(AI), particularly Machine Learning, has been used to resolve various real-life issues. But much of its potential is still untapped or are still unexplored. One such potential of Artificial Intelligence (AI) is definitely in the Healthcare Sector—particularly in the field of drug discovery.
Drugs are the cornerstone on which the Healthcare Industry rests. Due to the research efforts of recent times, knowledge of drugs and medicine has already grown exponentially. There are now new and advanced means of medicine production, and there is more knowledge of the diseases and their molecular structure that the drugs target. Due to these advancements in medicine and diseases, newer and better drugs are being made.
That is where Artificial Intelligence (AI) can come in handy. Indeed, Artificial Intelligence (AI) has already proved in its practical uses in various fields that it can go beyond confines risen by the traditional bottlenecks and revolutionize the field. So, is Artificial Intelligence (AI) possible to help create more effective drugs while reducing the cost and time required to make them?
Why Should Artificial Intelligence (AI) Be Used in Drug Discovery?
There are many reasons why Artificial Intelligence (AI) can benefit the pharmaceutical field. Some of them are mentioned below:
- The work of a drug to treat a disease is a complex one. The medication needs to target, through a protein, the specific molecules of illness. The drug compound has to find these molecules in the first place to eradicate them. There is a lot of trial and error. The efforts of trial and error should be minimized through Artificial Intelligence (AI).
- The various steps in making a drug that is synthesizing, designing and evaluating of the drug compounds, carried out by the chemists, can be done more effectively by Artificial Intelligence (AI), and it indeed remains an unquestionable factor.
- Currently, the cost of launching a new drug in the market is around 2.6 billion USD. It is also a time–consuming process. Artificial Intelligence (AI) can easily reduce both the cost and time involved in making and introducing a better drug in the market because of its data processing potential. Such is the added benefit that it offers in drug discoveries.
- It is reported by the research firm, Bekryl that Artificial Intelligence (AI) can save around 70 billion USD by 2028 in the drug discovery process.
What Can Be the Exact Application for Artificial Intelligence (AI) in Drug Discovery?
Drug discovery mainly involves screening a large set of drug compounds to eradicate the disease molecules. Individual researchers can’t do it promptly. Following are some of the unique features of Artificial Intelligence (AI) as to how it can simplify this cumbersome process:
- Artificial Intelligence (AI) is predictive. It can predict the properties of the ideal drug compound needed to eradicate the disease molecule. Thereby, it can easily eliminate the need to try out different drug compounds;
- New drug compounds can be invented automatically without analysing hundreds or thousands of histology images and other mundane, repetitive tasks;
- Deep neural networks of Artificial Intelligence (AI) can understand how the novel drug would react with the disease molecules. The main benefit of deep neural networks is that they can predict which drug compound is going to work or not going to work effectively with which drug molecule, but it also tells why it is not going to work.
Why is the Use of Artificial Intelligence (AI) in Drug Discovery Not Quite There Yet?
While Artificial Intelligence (AI) can accelerate the process of drug discovery, there are certain pitfalls as well. The researchers and scientists must remain abreast of all those notable factors as well. One that is particularly restrictive is that while the chemistry of the compounds, that is, their chemical make-up and what is working or beneficial about it, can be identified and also easily understood through Artificial Intelligence (AI), their reactions in the real biological environment cannot be predicted just as easily as that can be considered.
That is to say, chemistry can be done computationally, but computers cannot handle the biological part. There are several nuanced aspects of a biological environment as well. Take for example the factors like thermodynamics, amongst others. IT cannot be accurately replicated for Artificial Intelligence (AI). It is because even the researchers themselves cannot input details of a biological environment, such as receptor conformational changes, signalling and so on, to the dot.
More complex than biological environments are the changes in protein modifications or gene expressions to foresee and account for while using Artificial Intelligence for drug discovery.
In a nutshell, an ideal compound to react with a perfect disease molecule in an ideal biological environment can be created efficiently. However, the problem is that it is not so suitable for a practical use. Knowing such factors is definitely the need of the hour, yet continuing with the new researches for more finding of innovative ideas.