AI in Drug Discovery and Development: A Dream Becoming Reality

The human body is complex. Understanding this complexity has been the cornerstone of medical diagnosis and treatment. A drug can be inefficient, if it is not given in the right dose, or poisonous if the dose exceeds the amount at which it works.

But dosing is not what the only factor involved in the efficacy of a drug. The site of action matters too. Pairing a drug to the appropriate site of action (there are millions of sites of actions in the body) is still a work in progress. One wrong move and you get an undesired action.

However, health care professionals have done a fine job over the years by carrying out research that improves the outcome of this pairing, but it is still not enough. A useful part of AI is its accuracy. Now imagine the accuracy of AI in drug development and discovery. This implies that the pairing earlier would be more accurate, drugs will work better and side effects would be almost eliminated.

In recent years, because of breakthroughs in computing power and machine learning, there has been a change in the industry. The use of AI in drug development and discovery is more common than we had, say, 10 years ago. The outcome?

In this article we will see how the use of AI in drug discovery and development advances the quality of treatment, and the impact of this development to the health care industry.

Let’s get into it!

Specific AI developments in drug discovery development

The inclusion of artificial intelligence into drug development and drug discovery did not just come out of thin air. It started somewhere. Over the years, technological advancements have made it evolve into what we have today.
We mentioned at the beginning that the human body is complex, and as such everything concerned with the modification or improvement of the state of the body is not so simple either. Therefore, the development and discovery of drugs through AI is not so straightforward. There are in fact, multiple facets of the discovery and development pipeline. Luckily, we can broadly categorize these developments based on the aspect of drug discovery and development they fall under. These include:

Generation and optimization of molecules

The accurate pairing of a drug molecule to a biological target causes an effect. Traditional chemists largely relied on acquired knowledge and incremental experimentation to design the molecules that might interact with a biological target. It worked, but for a while, and came with its equal share of problems.
The involvement of AI in this step of drug development, particularly deep learning techniques aids scientists to easily identify molecules and compounds that most likely to succeed in clinical trials. Virtual AI screening and optimization also allows scientists to predict the interaction between the drug molecule and its intended biological target. With this, scientist can effectively optimize drug-target interactions before clinical trials.

Predictive modeling for drug efficacy and safety

Like we highlighted earlier, a drug can be ineffective or toxic at certain concentrations. The only way to ascertain the safe concentration is through trial-and-error. The trial-and-error process is usually a time consuming process, and may take months or even years before the safe concentration of a drug can be determined. Modern AI models cut this process short by analyzing the intricate chemical structures to predict toxicity profiles though molecular simulation and structure optimization. The rate of absorption and clearance, and even effects that may be undesirable can also be determined. The data gotten from this analysis helps scientists predict the outcome of a drug, and make necessary adjustments before the drugs are taken to trial.

Better understanding of biological targets

Biological drug targets often have complex structures that scientists cannot easily understand. When researchers fail to fully understand these targets, they risk developing drugs that produce little or no therapeutic effect. Today, scientists use AI systems to actively analyze complex biological targets, including genes and protein structures, to identify those most suitable for treatment.

These AI tools enable researchers to pinpoint specific proteins and genes and clearly define disease mechanisms that conventional methods previously failed to explain.

Clinical trials

Before drugs are approved by the FDA, they undergo stages of clinical trials. Participants of these clinical trials are chosen based on certain criteria which they must meet. The selection process, however, may not be adequate to select the ideal participants. This causes some trials to face slow recruitment.
Using AI in drug development and discovery has shown great promise in transforming the clinical trial process. AI models can analyze large datasets such as genetic information, demographics, medical histories and so on to predict which patient populations are most likely to benefit from the new drug.
AI agents also help to categorize the patients in the clinical trials into subgroups by identifying biomarkers that correlate with the treatment response. Each subgroup of patients contain patients that may respond differently to the drug being tested.

What is the impact of AI in drug discovery and development?

Integrating AI models into drug discovery and development is bringing about immeasurable impact in medicine and the health care industry in general. Some of these include;

Faster drug delivery timeline

The timeline of drug development takes an average of 12 years, but with the involvement of AI, this timeline can reduce to as much as 5 years. This study shows an almost 50% decrease in the time it takes from drug discovery down to FDA approval. With this, patients will get faster access to advanced drugs for treatment.

Cost reduction

Traditional drug discovery and development is expensive. Research shows that it can be as high as $2 billion. Bring AI models into the picture and there is a 75% decrease in the amount needed. This comes from elimination of resource wasting tasks and reallocation of these resources towards the most viable leads.

Enhanced precision and patient individualization in treatment

AI has the capacity to analyze multimodal biological and clinical datasets that are necessary for precise individualization of drugs. AI models individualize patients as a single person, and a single entity, unlike traditional methods which consider patients in a group. Patient individualization in treatment has been found to greatly improve therapeutic outcomes.

Increased probability of success in the clinical stages

Early knowledge about a compound’s safety and the interaction of the drug molecule with its target reduces the trial-and-error phase of drug development. This knowledge also helps scientists to accurately manage the toxicity profiles of the drugs. It has been known that unforeseen drug toxicity causes failure of clinical trials. However, knowing this beforehand is having foresight which will prevent failure.

Decentralization of drug research

With AI-driven tools, the barrier to drug research and discovery is lowered. This makes room for smaller firms, academic institutions and startups without expansive laboratory facilities or resources to try their hand at drug research. Decentralization brings about the reduction in the control of this industry by big pharma companies. As a result, smaller groups with cloud-based AI platforms can collaborate, experiment and develop. Decentralization encourages competition, and diversity of ideas which only helps to produce the best for outcomes patients.

Openfabric AI: a major shift towards AI-based drug discovery and development

Emerging technologies have helped to advance the shift from traditional drug discovery and development to AI-based drug discovery and development. One of such industries powering this innovation is Openfabric AI. Openfabric AI creates decentralized, and integrative frameworks such as the AI Drug Discovery Tool.
This AI application is one of many that supports and promotes the integration of AI into the health care system to improve patient’s outcomes. Unlike single AI models, this tool uses multiple specialized models to handle the different processes involved in drug discovery and development.

Each AI model in the AI drug discovery tool aims at:

• Novel molecule generation
• Toxicity prediction
• Drug-target interaction

This multi-model approach signifies a major shift in drug development and AI automation in general. Fully backed by a decentralized, Layer 1 AI protocol, the tool give startups and small companies the opportunity to create pharmaceutical solutions and experiment with the outcomes of these drugs.

Conclusion

The journey of AI in drug discovery and development has come far from where it started. With companies like Openfabric AI, it is safe to say that this innovation has more in store. Using AI in drug discovery is often a mix of AI techniques like Machine Learning, Natural Language Processing, deep Learning and so on. With these technologies leading these AI-powered approaches, there will be enhanced precision, efficiency and success in drug discovery and development which will ultimately improve patient outcomes globally.

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