The use of AI in drug discovery has transformed how drug discovery research is conducted within the pharmaceutical industry.
Previous drug discovery processes were very time-consuming and expensive; drug development can take anywhere from 10 to 15 years or more.
However, advances in AI technology have transformed the traditional methods of drug development.
AI technologies enable researchers to sift through large amounts of data and identify potential drug candidates much faster and with higher accuracy than traditional methods.
Computational Drug Design and Machine Learning are two AI technologies that enable scientists to identify drug targets more quickly and efficiently.
Generative AI enables the creation of new, unique chemical structures (molecules) designed to target specific disease states.
This enables the development of new treatment options for many diseases once considered untreatable, thereby opening a new avenue for drug discovery.
This article examines how AI enhances the drug discovery process and the long-term implications it may have for the future of medical treatment.
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AI in drug discovery by revolutionizing the historically slow and expensive development process of the pharmaceutical industry. For decades, the development timeline for new medicine ranged from ten years or longer and cost over $2.5 billion per drug.
However, because researchers had limited knowledge about diseases, genetics, and chemical compounds, they used an enormous amount of resources to test hundreds of thousands of chemical compounds through a “trial and error” method of drug research. That has all changed with the emergence of AI in drug discovery.
The use of AI in drug discovery is changing the paradigm of drug research. The use of large datasets on disease, genetic information, and compound chemistry in drug discovery enables the AI system to determine which compound is the most likely candidate for drug development based on its ability to target a specific biological target.
Prior to the emergence of AI in drug discovery, researchers spent substantial time testing chemical compounds in hopes of finding a successful candidate; conversely, AI in drug discovery serves as a rapid, efficient search engine to identify the best candidate for drug development.
Generative AI, by identifying viable drug candidates from data on diseases, genetics, and compounds, can generate new molecular compounds specifically designed to target a particular biological target.
Generative AI is also making it possible to explore areas that have been previously labeled as “undruggable”, where researchers may have previously attempted to develop treatments but were unsuccessful and therefore believed to be unable to be treated; generative AI in drug discovery offers new opportunities for the treatment of these previously considered undruggable diseases.
A predictive model is another tool commonly used in AI for drug discovery to simulate how a new drug will act in the human body before entering the lab. By doing so, AI in drug discovery can help mitigate many of the risks associated with clinical trials by enabling the real-time selection of optimal trial participants and monitoring for adverse effects.
AI in drug discovery has already begun to transform the way that pharmaceuticals are developed. Companies such as Insilico Medicine have now successfully used AI to bring drugs into clinical trials. AI in drug discovery will continue to revolutionize the development of pharmaceuticals, enabling accelerated timelines, reduced costs, and enhanced opportunities for personalized medicine based on an individual’s unique genetic profile.
It’s a common question we’ve asked ourselves: Why does it take so long to develop new medicines? In recent years, with the development of the first COVID-19 vaccines, the world has seen a global focus on this issue; however, it’s well known that the road from an idea in a laboratory to availability at a local pharmacy is long and arduous.
In fact, industry research reports show that the process of developing new drugs takes approximately 10 years and requires significant investment—over $2.5 billion per successful drug that finally reaches the market.
This challenge is rooted in a problem of enormous scale. Consider that you have to find a single specific key that will open a specific microscopic lock within the human body. Now consider that the key is buried among billions and billions of keys that appear nearly indistinguishable. This is the foundation of how traditional drug discovery works—i.e., a laborious, trial-and-error process in which a researcher is attempting to locate a “needle” in a cosmic haystack.
“However, what if there were a new partner in the lab to aid the scientists in their search? This would be a computer-based system called Artificial Intelligence (AI) that is poised to revolutionize how drugs are developed.
AI works as a guide, using the laws of biology and chemistry to find the “needle in the haystack.” In essence, AI enables scientists to quickly and accurately navigate the haystack of possibilities, offering a new, cost-effective strategy for drug development.
The new direction in drug discovery uses AI to enable researchers to identify the correct biological target for a potential new medicine, design an optimal molecular “key,” and predict the molecule’s success or failure before conducting laboratory experiments. The new direction of using AI to support drug development research will leverage both speed and intelligence, providing a new direction for pharmaceutical Research and Development.
The Old Maze vs. The New Map: How AI Transforms the Drug Discovery Journey
The search for a new drug had long been akin to an enormous maze, in which scientists moved, literally feeling their way along each path (with the aid of a blindfold). One at a time, the chemists would exhaustively test thousands of chemical compounds in the hope of finding one that could combat a particular disease.
The large majority of these trails were “dead-ends,” taking researchers years to complete costly research with no guarantees of success. The sole reason this trial-and-error approach to testing has taken so long to bring drugs from the laboratory to pharmacies is the time required to test every compound in vivo.
Artificial intelligence has dramatically changed the landscape of where we look for drugs. Rather than roaming around in a maze, artificial intelligence works like an incredibly advanced search engine for biology. The search engine has access to large volumes of data on disease, genetics, and chemistry. With that information, it can virtually scan millions of possible combinations of drug candidates in hours.
In addition, it is not merely scanning random combinations but learning the fundamental principles that underlie the effectiveness of medicines, and it will begin to predict the likelihood of success for each combination before it is developed in the laboratory.
This monumental shift from brute-force screening of potential drug targets to intelligent prediction enables researchers to bypass the two most time-consuming and expensive stages of drug development (i.e., testing a large number of possibilities and determining the effectiveness of one or several).
However, the new capabilities provided by this powerful technology raise a fundamental first question: Among all possible targets in the human body, how does the computer know where to “aim” its search?
AI Pharmaceutical Research: How Modern Drug Discovery Works
AI in pharmaceutical research is changing how research is conducted, delivering faster results and increasing the likelihood of success. AI Pharmaceutical Research uses artificial intelligence (AI) to enable researchers to quickly analyze large amounts of data containing biological, chemical, and clinical information to identify potential drugs for treating diseases.
The use of data to transform the drug discovery process is one of the most significant features of AI in pharmaceutical research today.
The typical drug discovery process involves a time-consuming trial-and-error approach, with scientists testing numerous compounds over extended periods. AI Pharmaceutical Research leverages computer technology to analyze complex biological data and predict molecular candidates with the highest probability of success in targeting a specific disease.
Using machine learning algorithms, AI Pharmaceutical Research systems can discover hidden patterns in data that may never have been uncovered through human research efforts.
AI pharmaceutical research will be able to identify existing drugs, create new ones using generative AI, and design them with the desired properties. By producing customized drugs, researchers can consider “undruggable” targets in their search for effective therapies, thereby accelerating research and development in this field.
Another major area of AI application in pharmaceutical research is AI-based predictive modeling, an integral part of AI Pharmaceutical Research, which enables researchers to model how drug candidates interact with biological systems before conducting expensive clinical trials.
In addition to reducing the resources wasted in clinical trials, this testing method can help reduce the risk to patients from adverse reactions by detecting potential drug issues during the development phase rather than after approval.
In the future, as more companies continue to use AI technology, we anticipate a rapid expansion of AI pharmaceutical research as it relates to drug discovery. Drug discovery will become faster and more efficient, enabling the development of customized treatments based on a patient’s specific genetic profile. Overall, this represents a new paradigm in innovation that may revolutionize health care and enhance patient outcomes.
Finding the Bullseye: How AI Pinpoints a Disease’s True Weak Spot
To determine whether your question can be answered, we must first identify the scientist’s objective. Most diseases (from infections to cancer) result from an abnormally functioning cellular component, specifically a particular protein; most drugs must directly bind to that protein. The “troublesome” protein is like a unique, microscopic “lock.” We require the “key,” the appropriate medicine to fit into this “lock.”
The problem is that there are many types of locks — about 20,000 — present in the human body. Finding the correct lock to target the cause of an illness, when only one of these locks is the actual culprit, has been the monumental first step in drug discovery for years. Choosing incorrectly will waste up to ten years of research and also fail in clinical trials.
AI becomes a powerful tool for biologists by analyzing large volumes of genetic data, patient medical histories, and research on living organisms. As such, it can detect subtle connections between a specific protein in the human body and a disease (connections that may be beyond human recognition). It sifts through all possible “suspects” and determines which “lock” (single point) is the most vulnerable or weakest link.
By quickly and accurately determining where to aim, scientists avoid the time-consuming, frustrating, and often unsuccessful process of wasting years investigating the wrong lead. In addition, the entire drug development process is focused on the appropriate “bullseye” from the first step onward. However, when AI finds the perfect “lock,” another larger question exists: how does it design a new “key”?
Machine Learning in Pharmaceuticals: Turning Data into New Medicines
Machine Learning in the Pharmaceutical Industry has the potential to transform the way drugs are developed and tested, leveraging large datasets to drive research aimed at developing new medications. The volume of data generated is increasing exponentially, and although the pharmaceutical industry is producing it, it’s increasingly difficult for companies to use traditional methods to identify new medicines.
Therefore, Machine Learning in the Pharmaceutical Industry is one of the few tools that can help identify and test new potential medicines faster than traditional trial-and-error approaches.
The ability of Machine Learning in Pharmaceuticals to discover new medicines relies heavily on large amounts of data from Clinical Trials, Genomics, and Chemical Databases, processed by various algorithms.
Machine Learning in the Pharmaceutical Industry uses data to learn from historical results and patterns to predict the efficacy of potential new compounds, thereby accelerating the AI-driven drug discovery process. This will enable scientists to quickly identify which candidate drugs have the greatest potential to succeed, thereby reducing the time and cost required to develop new medicines through traditional trial-and-error.
A major example of how Machine Learning has been applied in the pharmaceutical industry is drug repurposing. Through extensive clinical data searches, Machine Learning algorithms can determine which drugs have been used for indications other than their original use. The ability to rapidly identify drugs for various indications will enable patients with diverse health conditions to access treatment options more quickly.
Machine Learning in Pharmaceuticals also aids in the design of novel molecular compounds. Virtual compound designs are generated and analyzed through an algorithmic process; thus, the likelihood of discovering successful treatments for disease increases, while the risk associated with conducting experimental trials decreases.
An additional way in which machine learning plays a significant role in personalized medicine is by assisting in analyzing individual data, predicting treatment responses, and developing targeted therapies. To summarize, Machine Learning in Pharmaceuticals is a transformative technology that is transforming data into new medicines in a highly efficient and effective manner.
As this technology continues to evolve and improve, it promises to significantly accelerate the discovery of new medicines and improve the overall quality of life for millions of people suffering from a wide variety of medical conditions.
From Search to Creation: How AI Can Invent a Brand-New ‘Key’
Historically, scientists were on a “numbers” game in their search for a new “key,” i.e., a small molecule (a drug). They would use a library of known molecules and aim to identify a few thousand or a million molecules that fit the “lock.” This was an extremely time-consuming, hit-or-miss process and very often resulted in no “keys” being found when the “locks” were particularly “unusual.”
Generative AI represents a paradigm shift AI in drug discovery; you are likely familiar with how it creates images, music, and/or text. Generative AI also works similarly in drug discovery, but instead of creating new images, music, etc., it generates new chemical compounds based on the chemical/biological principles it has been trained to understand. Thus, rather than examining all possible keys, the AI can design a novel compound from scratch, with the optimal shape and properties to fit the lock.
The potential impact is vast. Over the past few decades, most diseases have been deemed “un-drug-able” because they have unique target protein structures that no known compounds can interact with. However, generative AI has enabled overcoming these barriers. As it does not rely on existing molecules, generative AI can design a custom-made key to open drug targets that were previously considered inaccessible and may be used to treat diseases once thought incurable.
In addition to discovering new keys, the ability to generate new keys (and, by extension, new treatments) is likely the largest paradigm shift in medicine in history. While creating a new key is an important part of developing a treatment, it is only half the equation. Scientists also need to determine if a new molecule will act as intended when tested within the complex environment of a living organism. Therefore, the second part of the process is to use AI as a simulation tool to test drugs before laboratory testing.
Computational Drug Design: How AI Simulates Molecules Before Lab Testing
Computational Drug Design has dramatically changed the way drugs are developed, using Artificial Intelligence (AI) to model molecular interactions before actual laboratory trials of new drug candidates. The ability of researchers to virtually test drug candidates greatly accelerates their identification, making Computational Drug Design an increasingly important component of pharmaceutical research and development.
Molecular modeling and molecular dynamics simulations are two of the primary methods used in computational drug design to analyze large datasets on the chemical and biological properties of target molecules.
Using these methods, researchers can model the interactions between candidate drug molecules and their intended biological target(s), ultimately enabling them to pre-identify the best candidate molecule(s) from a large pool, thereby minimizing the “trial and error” aspect of traditional AI in drug discovery methods.
Computational drug design offers several advantages, including the significant reduction in the time and cost of conducting laboratory experiments. Through virtual testing of thousands of compounds, researchers are able to assess their potential as drugs based on both efficacy and safety before investing in the laboratory synthesis of the compound(s) to be tested.
The use of computational drug design accelerates drug discovery by enabling researchers to identify potential candidates that might be missed under traditional approaches.
Computational drug design using artificial intelligence (AI) enables researchers to study complex biological systems and determine the interactions between a drug and other factors, such as environmental or genetic conditions. Based on this information, researchers can optimize the molecule’s structure, thereby developing safer, more effective drugs.
As an increasing number of pharmaceutical companies adopt computational drug design within their AI-driven drug discovery pipelines, the future of drug discovery appears very promising. As AI begins to simulate drugs prior to laboratory testing, it will significantly increase the speed, efficiency, and overall success rate of developing new treatments for the marketplace.
Generative AI in Drug Discovery: Creating New Molecules with Intelligence
Generative AI in Drug Discovery is a promising area of innovation because it represents a paradigm shift in the design and development of new drugs. In contrast to the long-standing method of testing the efficacy of existing compounds by screening them against potential biological targets, generative AI uses machine learning to generate new molecular structures from scratch that are specifically targeted to these targets.
The use of generative AI in drug discovery will open entirely new areas of pharmaceutical research that were previously inaccessible for the treatment of “undruggable” diseases due to their complexity.
Generative AI in drug discovery relies on training large numbers of algorithms using vast amounts of data (e.g., chemical libraries, biological data, and clinical outcomes) to generate predictions. Using the knowledge gained from these large datasets, the generative model will generate novel compounds with desired characteristics (e.g., increased efficacy and reduced adverse reactions).
The development of new drugs using generative AI is much faster than traditional methods; moreover, its use in drug discovery enables a broader creative base for designing new molecular entities.
Generative AI in drug discovery will enable researchers to discover compounds across an almost infinite chemical space with far fewer constraints than traditional drug development methods. The researcher inputs the desired characteristics or parameters (or constraints) for the compounds, and then receives a list of candidate drugs that meet those criteria.
Generative AI can also help optimize drug candidates by rapidly and iteratively combining modifications to identify the optimal attributes of each. As a result, only candidates with the highest probability of success are tested in the lab, thus reducing the time and money required for drug development—a major advantage of using generative AI in drug discovery.
The potential for innovation in the pharmaceutical industry is enormous as the use of generative AI in drug development continues to grow and evolve. The possibilities for developing new therapeutic treatments for previously untreatable diseases are endless, as this technology greatly expands
The ‘Digital Flight Simulator’ for New Medicines
Although scientists can test a brand-new medication in an entirely digital world, much as pilots train for numerous hours in a flight simulator before flying a single actual airplane, predictive modeling is a way for a scientist to test an idea for a new medication before developing it in a lab. Predictive modeling employs artificial intelligence (AI) to generate a virtual environment that simulates the complex biochemical interactions in the human body.
The use of a virtual environment allows scientists to ask critical “what-if” questions and observe the predicted behavior of a new molecular design before creating it in a physical lab.
When an AI model is trained on vast amounts of biological data, it learns the rules governing interactions among different molecules and cellular components of the body. Therefore, when a scientist presents the AI with a new candidate drug molecule, the AI can simulate numerous possible scenarios of the drug’s future performance.
For example, will the candidate drug bind to the target molecule? Additionally, are there possibilities that the candidate drug could interfere with healthy bodily systems, causing adverse side effects? The predictive capabilities of AI models provide scientists with an incredible tool for distinguishing between viable and non-viable ideas, ultimately eliminating the need to pursue potentially toxic or ineffective compounds.
While the application of predictive modeling is powerful in assisting scientists in identifying the best candidates for advancement to human clinical trials, its most significant advantage lies in reducing the number of failures experienced by scientists. Historically, most candidate drugs have failed after testing in expensive, time-consuming laboratory environments.
Using predictive modeling, scientists can evaluate thousands of potential candidate drugs in a virtual environment before investing resources in physical laboratory testing. In doing so, scientists can eliminate candidates with the least potential for success at an early stage and focus their efforts on those with the greatest potential.
Ultimately, this reduces the number of candidate drugs advanced to the next phase of development—the human clinical trial phase—and increases the likelihood that successful candidates reach the market.
AI Drug Development: From Discovery to Clinical Trials
The entire process of developing new medicines (from drug discovery to clinical trials) is being transformed by AI-driven drug development, resulting in greater efficiency and effectiveness at every step of the development pipeline.
The traditional pathway for drug development, from the conception of a drug candidate to approval for sale as a medicine, has historically taken an extended period of time and cost many billions of dollars, and typically takes longer than ten years. AI Drug Development is now integrating AI-based technologies across all stages of drug development, significantly accelerating the development process.
AI Drug Development is also improving the AI in the drug discovery phase of the drug development process. AI Drug Development technologies leverage large datasets containing biological, chemical, and genetic data on biological systems and use machine learning algorithms to identify potential drug candidates. Thus, these AI Drug Development technologies can eliminate much of the need for “trial-and-error” methods used to identify potential drug candidates. As such, researchers can quickly narrow their research focus to the most promising drug candidates earlier in the drug development process.
AI Drug Development plays a significant role throughout the drug development process, optimizing the molecular design of identified viable compounds and predicting their behavior in biological systems. Predictive modeling, therefore, enables only the best candidate drugs to be advanced to laboratory testing, thereby reducing the time and costs required to develop drugs through traditional means.
In addition to identifying the optimal candidate drug, AI Drug Development assists in selecting patients for clinical trials by analyzing patients’ medical histories to identify those who best meet the criteria for specific genetic and biological markers.
AI technology also monitors real-time data from each participant in the study, enabling researchers to rapidly detect adverse effects or lack of efficacy, protect participants, and shorten trial duration. As a result of these developments, an overall more efficient and productive drug development pipeline exists through AI Drug Development.
The future of this field is continually evolving; as such, AI may lead to changes in the pharmaceutical industry, providing quicker access to novel treatments, improving patient care, and enhancing outcomes. Thus, in summary, AI Drug Development is leading to a more efficient, targeted approach to developing new drugs and bringing them to market.
Making Human Trials Faster, Safer, and More Effective
In addition to passing the “Digital Flight Simulator” test, a new drug candidate faces one last major obstacle: the Clinical Trial Phase, in which a drug is tested on humans.
The Clinical Trial Phase is typically the longest and most expensive of all phases a drug undergoes, as finding and recruiting the appropriate volunteers (often referred to as subjects) and protecting their health and safety can be time-consuming and may last for many years. However, AI is beginning to make a significant impact in this area as well.
AI is enabling researchers to address the challenges of the clinical trial process by utilizing three primary methods:
- Selecting the Right Trial Participant: AI technology can quickly sift through large pools of anonymized health data to identify individuals whose biological characteristics match those of the target population (e.g., specific biological marker(s) and/or disease stage). The result will provide the optimal conditions for producing reliable, accurate results in a clinical trial.
- Detecting Adverse Reactions Earlier: Wearable devices, such as smartwatches, will provide continuous data that can be reviewed using AI to detect potential adverse reactions before participants are aware of them. This will create an additional layer of protection for trial participants.
- Processing Trial Data Faster Than Ever Before: AI technology will enable the real-time analysis of data collected during clinical trials to reveal insights into the effectiveness of a particular drug. In addition to accelerating time-to-market for potential drugs, AI technology will enable researchers to obtain answers to their questions about a drug much sooner than they would without it.
Overall, AI will facilitate a faster, safer, and more personalized clinical trial experience. By identifying the best candidates for clinical trials and continually monitoring participants’ conditions, AI technology can help close the gap between laboratory research and the delivery of medications to pharmacies nationwide.
Success Story: How AI Designed a New Drug Now in Human Hands
The development of a potentially life-saving treatment for a devastating disease is not some far-off fantasy – it is happening today. At the vanguard of that work is Insilico Medicine, which has entered human clinical trials with a drug developed by an artificial intelligence (AI) system for a serious lung condition that permanently damages the lungs. The work represents another important step in the history of medicine, as it is among the first instances of a fully AI-designed drug entering late-stage clinical trials in humans.
Insilico’s AI performed two critical roles: that of a scientist, who sifted through a vast amount of data on biology to identify the particular protein – or “lock” – responsible for the permanent damage to the lungs. And then the AI took the role of an inventor and created a completely new chemical compound – or “key”- that no human had ever made previously.
The entire search for the key and the creation of the lock took place in less than 30 months, a full decade faster than it typically takes for discoveries of this type. While a single successful example can serve as a powerful model for how pharmaceutical R&D will evolve, it also demonstrates that when scientists work with smart machines, they can produce results at a rate once believed unachievable.
And if the collaboration between humans and machines can lead to a new medication for a complex disease so quickly, then what else can be done? This sets the stage for even more extraordinary potential: medications tailored to individual needs.
Drug Discovery Innovation: How AI Is Reshaping the Pharmaceutical Industry
Drug Discovery Innovation is undergoing an extraordinary transformation due to the emergence of Artificial Intelligence (AI) technologies, marking the advent of a new generation in the Pharmaceutical Industry. For decades, traditional drug discovery processes have been hindered by extended timelines, enormous costs, and extremely high failure rates.
The advent of AI technologies will significantly alter the way researchers find, design, and bring new drugs to market, thereby accelerating Drug Discovery Innovation across the industry.
AI now affects every phase of Drug Discovery and Innovation, even at the earliest stages. In identifying disease targets, AI systems can rapidly analyze large volumes of biological data to uncover associations between diseases and potential drug targets, thereby significantly improving the speed and accuracy of target identification for drug discovery. Historically, drug discovery Innovation has relied on trial-and-error; thus, this early process has also been reduced.
The advancement of AI in Drug Discovery Innovation occurs after target identification, as AI enables the molecular design process with generative models that can generate novel compounds based on desired characteristics. The algorithms enable researchers to search through the vast chemical space, potentially uncovering novel molecules that may be unattainable through traditional approaches. As such, it could be possible to discover treatments for “undruggable” diseases.
In addition to its role in molecular design, AI plays a vital role in predictive modeling, supporting drug discovery by simulating how new drugs would behave in the body. Researchers can determine whether potential side effects exist or whether they need to adjust their drug candidate before conducting expensive laboratory tests by using simulations to model the drug’s interactions in the body.
In addition, AI-driven analytics improve efficiency in clinical trials by enabling better participant selection and real-time monitoring of adverse events.
AI-driven drug discovery transforms the process into an even more agile, focused approach, resulting in faster time-to-market for drugs and lower drug development costs. With the ability to analyze large sets of information (data), AI will be able to expedite Drug Discovery Innovation so that pharmaceutical organizations can react much faster to newly-emerging public health issues.
As Artificial Intelligence continues to evolve, it will have a significant transformative impact on AI in drug discovery, enabling the pharmaceutical sector to create additional opportunities to develop new treatments that will ultimately enhance global health outcomes.
Why Your Future Medicine Might Be Made Just for You
The world has been told of how there is a medication that can cure one person with a particular illness and have no effect on another. This is due to the fact that most medications being prescribed today are made for the “typical” or average person. A typical example is buying a suit off the rack; it may fit some people very well, others only fairly well, and still others poorly. Personalized medicine aims to address this issue by providing each individual with their own “tailored suit”.
Personalized medicine will provide a “custom-fit” by using an individual’s unique biological instructions (DNA) as its guide. Genomics is the study of these instructions and the minute differences within them that make you who you are.
The genetic code also explains why an individual’s body may respond differently to a medication than another individual’s. The problem is that our genetic code comprises billions of letters, making it impossible for any physician to examine and analyze every patient.
Artificial Intelligence will be instrumental in tailoring health care to each individual. The AI will act as a “master decoder,” quickly scanning the person’s genetic data and comparing it with vast databases of biological data to identify key patterns in their genetic makeup. The AI will be able to determine which ‘key’ is most likely to unlock a person’s genetic ‘lock’ before prescribing.
This could be revolutionary for diseases such as Cancer. Chemotherapy is typically a one-size-fits-all. However, an AI may be able to assess a patient’s tumor’s genetic vulnerabilities based on its genetic profile. Then doctors would use this information to prescribe a treatment specifically designed to target that type of cancer, improving the patient’s chances of success while minimizing adverse reactions. There is great potential here; however, many obstacles remain before it becomes part of daily life.
The Big Hurdles: Why Isn’t Every Drug Made by AI Yet?
There are many barriers to creating an effective “routine” use of AI drug discovery in medicine today, but the largest barrier by far is data. Just as a student with a high IQ cannot learn without good books, AI systems require high-quality scientific data to draw sound conclusions.
As long as the data scientists provide to train their AI systems is of poor quality (messy, missing, or inconsistent), the AI system’s conclusions will also be of poor quality. Therefore, before AI reaches its full potential for discovering new medicines, researchers must first clean up and standardize the vast amount of biological data collected over decades, a monumental undertaking in terms of cleaning up the “digital homes” of all areas of life science research.
Beyond the data lies the “black box” problem. Sometimes, an AI system may be capable of generating an excellent response (for example, a potential new drug) without being able to provide insight into how it arrived at that response.
This is similar to an individual who has the correct solution to a problem but cannot demonstrate their reasoning. The inability to provide clarity into an AI system’s decision-making process is a major concern for physicians and regulatory bodies.
To ensure patient safety, regulatory bodies require that the rationale for drug development is well understood and not based solely on the AI system’s statements; they also want to understand why the drug is likely to be effective. As such, scientists are developing more understandable, or explainable, AI systems to address this challenge.
Lastly, while integrating AI systems with perfectly accurate data and a completely transparent decision-making process would represent a tremendous step forward in the application of AI within pharmaceutical research, medicine represents a domain that will proceed with caution, and rightfully so. New medications are thoroughly vetted by regulatory agencies before being approved for market entry to ensure they are both safe and effective.
The integration of a revolutionary tool like AI into the current, structured environment of clinical trials and regulatory approvals will take time. While these are barriers to entry, they are important safety measures that facilitate the transition to a world where the obstacles to incorporating AI into pharmaceutical research are diminished, offering new hope.
A New Era of Hope: What AI-Powered Medicine Means for Our Future
The long winding road of developing a new drug has transitioned into a puzzle filled with many challenges. The biggest challenge remains finding the correct “lock” within the body and constructing the ideal molecular “key”; however, AI has become a new key player (partner) to help researchers complete this task.
This is not simply about technology; it is about time and, possibly, a glimmer of hope. This could mean shortening the time spent searching for cures and enabling life-saving drugs to reach patients more quickly. Additionally, this could enable researchers to investigate disease processes previously considered “undruggable” and replace the expensive trial-and-error approach to drug development with an intelligent, targeted drug-discovery process.
The goal of this framework is to provide context for news stories about how artificial intelligence (AI) is likely to impact the development of medicines using pharmaceutical R&D. The idea that a significant advancement in AI-based drug discovery is an example of scientists identifying better targets, developing superior drugs, and making more accurate predictions than they could before AI became available, is a story of great promise and potential opportunity.
AI will not replace the genius researchers in laboratories, but rather will help them achieve much greater possibilities as a collaborative tool. As we begin to see the potential to treat many rare medical conditions and achieve our long-held dreams of personalized medicine, we are moving from science fiction to the start of what may become a new standard of care and hope for humanity.
