Artificial intelligence (AI) is increasingly being used in different areas of society, notably the pharmaceutical business. In this review, we highlight the use of artificial intelligence (AI) in various sectors of the pharmaceutical industry, such as drug discovery and development, drug repurposing, such use reduces human workload while meeting targets in a short period of time.

We also address crosstalk between AI tools and approaches, current difficulties and solutions, and the future of AI in the pharmaceutical sector.

Why AI is used for Drug Discovery?

The primary objective of drug discovery services is to uncover medications that have a favorable effect on the body in other words; medicines that can help prevent or treat a certain illness. Traditionally, researchers conduct extensive screens of libraries of compounds to discover one with the potential to become a medicine.

They then put this through a series of tests to see if it’s a potent chemical. More rational structure-based drug design techniques have recently become more popular. These bypass the first screening steps but still require scientists to develop, synthesize, and test a large number of chemicals.

Because of recent advances in machine learning, the discipline is now ready to employ algorithmic methods for molecular property prediction to find unique structural classes of potential therapeutic candidates. Indeed, adopting techniques that allow early drug discovery to be done mainly in silico allows for the study of huge chemical regions that are now outside the scope of existing experimental procedures.

With recent developments in machine learning, the discipline is now prepared to use algorithmic methods to predict molecular characteristics and discover novel structural classes for drug creation.

Because it is often uncertain whether chemical structures will have both the necessary biological effects and the characteristics required to become a successful medication, developing a promising molecule into a drug candidate may be both costly and time-consuming.

Furthermore, even if a novel medication candidate shows promise in laboratory testing, it may still fail in clinical trials. Given this, it is not unexpected that specialists are increasingly looking to AI systems’ unrivalled data processing capability as a means to speed and lower the cost of identifying new medications. Merck and Bayer are two of the major pharmaceutical firms that have established collaboration with Cyclica to help them with medication discovery.

The selection of a target for a medication to treat is the first step in the drug discovery process. For complicated illnesses, this necessitates a thorough understanding of how molecules interact with one another.

This is where artificial intelligence comes into play. For example, Cyclica has developed software that analyses the biophysical (through surface plasmon resonance technology) and biochemical properties of millions of molecules to the structures and properties of about 150,000 proteins in order to find chemicals that are likely to bind to a target2.

Role of Artificial Intelligence:

Drug development is an expensive, time-consuming, and gradual process that begins with the discovery of a successful molecule and concludes with the final new molecular entity. The early stages of R&D in the drug discovery process might last up to six years. The following round of clinical testing often takes more than 5 years.

Only 10 out of 10,000 initially examined candidates for new medications make it to clinical trials during this time period. Hence at the end of this lengthy drug design process, authorities only approve one out of every ten medical items that go through clinical trials for use in patients.

The advancement of computer technology has sped up drug research and development. Artificial intelligence is widely used in various sectors and in academics. While machine learning is an important component of AI, it has also made its way into other fields, such as data production and analysis.

Numerous promising innovations, such as deep learning, aided in the development of self-driving cars, accelerated methodologies for speech recognition and translation into text, and aided with support vector machines, which may be described as supervised learning models with accompanying learning algorithms for classification and regression analysis.

Machine Learning is quickly becoming a must-have tool in medication research and development among preclinical CRO. AI platform designs can handle massive quantities of data, assisting researchers in drug discovery and development by giving important insights.

We will cover some artificial neural network designs that have been utilized for ML applications such as classification and regression analysis for medication development in the following paragraphs. To make the best selection for ML projects, it is critical to grasp the intricacies of each method.

Exscientia Exemplification:

Exscientia is a global Artificial Intelligence AI-driven drug discovery business that combines the power of original AI with a practical understanding of how to study novel therapeutic targets.

This artificial intelligence-created new drug candidate, paired with extensive skill, knowledge, and experiences in chemistry and pharmacology on monoamine GPCR drug discovery, might be the first AI-discovered new medication entity filed to treat obsessive-compulsive disorder.

Example of BenevolentAI:

This is another forward-thinking pharmaceutical firm that utilizes AI algorithms to analyze huge volumes of fundamental research data from public and private sources, illness mechanism comprehension, and medication target identification. As a result, they bring together the distinct abilities of over 200 experts, including biologists, chemists, engineers, informaticians, and data scientists who have pioneered precision medicine and drug development.

Bottom Line:

Finding novel ways to study huge chemical areas that are outside our current experimental procedures becomes increasingly important as the science of drug discovery and development advances. AI-based solutions that add a new layer of computer intelligence to current techniques can help us achieve this.

Prescription medicines have the potential to alter a patient’s life and give them the opportunity to recover from a life-threatening disease, making the development and approval of these treatments essential and important. However, in the last ten years, the process of medication approval has gotten more difficult and costly.