International Journal of Research and Development in Pharmacy & Life Sciences
Open Access

Artificial intelligence; Drug development; Technology; Drug discovery

Introduction

Big data and AI-driven analytics have changed the pharmaceutical industry's innovation paradigm. Machine learning, according to Nagy et al. (2019), can encourage innovation, optimize productivity, and produce stellar outcomes across every phase of the value chain. Sets breakthroughs and facilitates the development of new business models may significantly enhance the value proposition of the medical business. Managers of drugs are searching for methods to apply machine learning as well as artificial intelligence in biotechnology with health industries. Artificial intelligence (AI) holds great potential for adjusting the commercial operations picture of the pharmaceutical industry. There are concerns that larger businesses are applying the software applications that are there now, which is setting up the digital future of this market. Recognized drug firms work with healthcare professionals in artificial intelligence (AI) to apply AI to general drug discovery, development, and research [1].

Reports state that 62% of healthcare businesses want to make an immediate investment in AI, and 72% of them believe the technology will be necessary for their upcoming business procedures. The most important use cases for artificial intelligence (AI) and artificial intelligence (AI) in the sector, along with the outlook for the future, provide a more comprehensive understanding of the technology's future [2].

The sheer amount of data obtained, in particular in the field of pharmacy, has grown significantly every day. The phrase "big data" is becoming more and more common in many different fields of study. Moreover, businesses that use data are showing how large amounts of data could aid a variety of industries. Additionally, several meanings have been put up for the phrase "big data." "4 vs...." is among the most well-known definitions. The definition, provided by Douglas Laney, includes the "3 Vs" of volume, velocity, and variety. Later on, IBM added the fourth "V" to this definition. But there isn't any realizable, mutually agreed-on meaning for "large data."

The term "new oil" was allocated to it because of the potential value that lies in the data. The financial position and diversity of "big data" are affected by several avenues, such as digital platforms, user content, genetics, textbooks and publishing, technological health, and wireless sensor networks. Technical and data storage innovations are to blame for the sharp increase in data volume. Over 2.5 million individual papers in science are published each year.

Moreover, a little over 15,000 objects in the "pharmaceutical industry" name have been purchased in 2019.

Thus, "big data" could potentially be deemed both a bonus and a challenge in the pharmaceutical sector. The creation of modern machine learning technology has increased the potential uses of big data in the pharmaceutical sector by enabling computers to "learn" and undertake tasks. Considering machine learning is the greatest widely utilized AI technology in the healthcare industry, its breadth is unique. Several healthcare environments, such as robotic surgery, patient monitoring, and disease diagnostics, have observed an upsurge when using more artificial intelligence subfields such as the processing of natural language (NLP), expert systems, and robotics. However, compared to machine learning, these methods have not received the same substantial attention from the pharmaceutical industry [3].

History

Herbert A. Simon and Allen Newell introduced the logic theorist. Dartmouth College convened the renowned conference for the first time since 1956. Between the years 2022 and 2017, the artificial intelligence market will be expected to yield up to ten times as much revenue as formerly forecasted [4]. It is believed that in 2017, the natural language processing market, which includes several uses that involve speech, voice recognition, and text prediction, will grow by 28.5%. Global sales from business analytics and large-scale data mining reached US$122 billion in 2015, and by 2020, this figure is projected to increase to US$200 billion. Artificial intelligence has enjoyed a bumpy history since the 1950s. The idea that coding was primarily for dreamers started to change after IBM's Deep Blue overcame Kasparov, Garry in the 1997 chess championship. The $1 million Jeopardy prize was successfully won by IBM's brand-new Watson supercomputer in the US in 2011. Watson has After making a move into the pharmaceutical and medical sectors. To accelerate the creation of novel immuno-oncology medications, Watson partnered with Pfizer in 2016. Researchers can use the cloud-based IBM Watson platform to analyze dynamic visualizations to find links across different data sets. It was initially released by IBM and Pfizer in December 2016 [5,6].

 Artificial intelligence in drug discovery

Drug development involves the time-consuming procedure of testing compounds against samples of sick cells. To find compounds that are physiologically active and worthy of more research, more work must be done. Novartis research teams estimate which untested chemicals would be worth further investigation using pictures generated by machine learning systems. In addition to making new and effective treatments more readily available sooner, computers are finding new data sets drastically more quickly than classical human review and laboratory research. This discovery also results in cheaper operating expenditures than when each item is manually studied. The top pharmaceutical companies' present AI projects consist of [7]:

• A mobile platform that boosts medical treatments through real-time data-taking and delivering patient decisions [8].
• Drug discovery: chemical companies and IT companies have worked together to utilize the latest innovations in the pricey and drawn-out process of making medications [9].

Advantages of AI technology

The applications and AI's nature are challenging. It is an interconnected synthesis of computer science, mathematics, and other disciplines. The complexity of programming empowers computer systems to recreate human intelligence levels. The main advantages include [10].

An error prevention

• Metal bodies and protest make intelligent robots more able to withstand the harsh atmosphere of space, which makes them suitable for transferring on space curiosity missions.
• AI also helps humanity make fewer lapses and increases the likelihood of connecting with proficiency with greater precision.

Difficult exploration

This technique is helpful for the extraction and fueling of exploratory enterprises.

• AI systems may be able to explore the seas to get beyond human limits.

Through the robots' programming, they can carry out more demanding and tiresome tasks without getting tired [11].

Regular use

• AI has been utilized in daily life; • GPS has become commonplace and is useful for long-distance travel. When AI is installed on an Android device, it predicts what the user will enter and remedies typos. Think Lady Siri and the Cortana robots, for reference.
• An artificial intelligence algorithm recognizes and adds a face tag to images posted on social media platforms like Facebook or Twitter.

Financial and banking organizations frequently utilize artificial intelligence (AI) systems to manage and organize data to identify scammers [12].

Digital assistants

Organizations deploy AI systems' "avatars," Which are images of digital aids, to lessen their reliance on human resources.

• Because the avatars lack emotion, they reason logically and arrive at the proper conclusions. Emotions in people are frequently associated with emotions that hinder judgment and decrease output.
• Machine Knowledge was not used to observe this issue.

Repetitive job

Others are only able to focus on one task at a moment.

• It is possible for machines to multitask and think faster than humans.
• They may be programmed to execute hazardous activities and have their speed and time parameters changed.

Use in medicine

• The AI software involves instructing medical professionals about various drugs and their side effects.
• Doctors are using AI these days to screen patients and analyze health risks.

Accelerate the rate of technological advancement

• AI technology facilitates access to a world of more sophisticated technical advancements.
• The AI system may produce millions of computer modeling programs that support the search for new chemical compounds and entities. Consider QSAR and QSPR. for instance [13].

Disadvantages of AI technology

Expensive

• Due to the complex equipment design, maintenance, and repair, the deployment of AI involves a substantial expenditure.
• Software has to be updated on the system regularly.
• It costs a lot of money and effort to repair and recover the system. The R&D division needs a lot of resources, including time, to develop a single AI system. To repair and recover the system. The R&D division needs a lot of resources, including time, to develop a single AI system. To repair and recover the system. The R&D division needs a lot of resources, including time, to develop a single AI system.

No human replication

• AI-powered robots are capable of thinking similarly to humans, but they also lack morality and feelings.
• As a result, they carry out the assignment as instructed and are incapable of making a decision. Sometimes, it leads to major problems.
• Robots cannot determine whether they lack familiarity with the difficulties.
• They either supply erroneous information at that point or break down.

No increase in expertise

• AI-powered machines cannot be enhanced like human beings with experience.
• There is no concern, caring, unity, or belonging among machines.
• They are unable to distinguish between those who work hard and those who do not.

Joblessness

• If machines take the place of people in every industry, it would result in widespread joblessness.
• People are inherently very dependent on one another. They thus grow lethargic and lose their creative spark [14-16].

Types of artificial intelligence

The concept of artificial intelligence is wide and can be classified in several ways. Based on their level of quality, AI systems fall into the following categories:

Artificially narrow intelligence (ANI)

Often referred to as weak intelligence, this is the term for a system that has been trained and designed to do a certain task, such as facial recognition, chess, driving, or traffic signals. Take using Apple SIRI, the virtual personal assistant, and social media tagging using Apple SIRI, the virtual personal assistant, and social media tagging as examples.

Artificial general intelligence (AGI)

Human-level AI is another term for strong AI, which is often referred to as artificial general intelligence (AGI). It can make the intellect of humans simpler. It may therefore address issues when presented with fresh duties. AGI can do every human function.

Artificial superintelligence (ASI)

This is a mental capacity that surpasses that of intelligent humans in drawing, mathematics, space, and other domains. It can range from a computer that is only marginally smarter than a human to a computer that is a trillion times smarter than a human [17,18].

AI development of expert systems

This entails developing advanced, automated technology that can point individuals in the direction of the optimal course of action. Artificial Intelligence Applied to Computers: Consequently, similar cognitive processes will emerge in computers, enabling them to act like people and choose the right course of action when faced with challenging situations. Algorithms will allow for automated processes, which will lighten the burden on humans.

Multi-domain application

AI will be beneficial to a wide range of subjects, including computer science, cognitive science, statistics, psychology, engineering, ethics, the natural sciences, healthcare, space technology, logic, and linguistics.

Applications in computer science

Artificial Intelligence (AI) is used to develop a wide range of techniques for computer science applications, including search and optimization, logic, control theory, language analysis, neural networks, classifiers, statistical learning methods, and unpredictable methods for uncertain reasoning [19,20].

Classification of AI

• Artificial Narrow Intelligence (ANI) or Weak AI: It performs a restricted range of functions, including traffic signals, chess practice, automobile driving, and facial recognition.
• Artificial General Intelligence (AGI) or Strong AI: It is known as human-level AI because it does every task just like a person would. It can do difficult tasks and simplify human intelligence [21].
• Artificial Super Intelligence (ASI): It is more active and clever than humans in areas like as art, mathematics, and space exploration. The following classifications of AI may be made based on whether or not they are currently in use [22].

Recent trends

Pharmaceutical Automation in Research and Development

Artificial intelligence is a very recent notion. Even though laboratory automation technologies have existed since the 1990s, large-scale biorepositories, automated clinical and analytical testing, combinatorial chemistry, high-throughput screening, and other related activities have only recently begun to take off in these laboratories. Thanks to advancements in robotics and other technologies, a fully automated library is now a reality. A robust automated information system is necessary due to the high speed and volume of samples being handled, which results in an enormous flow of measurements and data. LIMS (laboratory information management system) is one example [23].

The automated infrastructure for the pharmaceutical sector is operational. The roles that have already been automated will continue in their position thanks to the little advancements brought about by the regular cycle of technological progress.

PAT (Process analytical technology)

Another cutting-edge trend is PAT. This is essential in supporting pharmaceutical businesses' efforts to enhance their production processes through innovation and a continuous improvement mindset. Patients save money as a result of increased product yields, better usage, and less waste. PAT encompasses more than simply hardware. To comprehend crucial process parameters, it has to be able to communicate with and collect information from numerous sensors and analyzers. It also has to carry out intricate multivariable computations and modeling [24]. 

Computational fluid dynamics  

Computational fluid dynamics allows product designers to evaluate several concepts rapidly and affordably. This method involves solving equations governing fluid flow numerically. To arrive at an overall solution, the domain to be investigated is first divided into millions of tiny 3-D cells called a computational mesh. From there, the basic equation (Navier-Stokes) for fluid flow is automatically solved. By adopting a 3D model of human physiology, CFD can help address the following problems and enable the examination of different drug delivery procedures quickly and affordably [25].

• Historically, drug discovery systems have been assessed by animal research. Nevertheless, conclusions are viewed as lacking sufficient credibility when generalized to humans, who have somewhat distinct respiratory physiologies, for example. This frequently results in significantly different dosages being released from a similar starting sample, which challenges meaningful comparison.
• There is an administrative requirement to reduce the amount of animal experimentation, limiting the extent of testing that may be undertaken.

AI technology start-ups in the pharmaceutical industry

The pharmaceutical industry has begun to harness artificial intelligence (AI) to aid in the competitive and extremely costly pharmaceutical development and research process. Large datasets, including illness patterns, may be effectively identified by AI solutions, which can also assist with figuring out the most successful chemical compositions for treating various ailments. The chart that follows includes some of the top businesses and startups in the world that use AI algorithms to provide pharmaceutical goods and services linked to artificial intelligence.

Standigm

The pharmaceutical sector has always been at the forefront of innovation, looking for fresh approaches to creating medications that might enhance patient outcomes. However, the process of developing new drugs is a drawn-out and intricate one that may cost billions of dollars to fund and take years to finish. For this reason, a South Korean start-up called Standigm has developed a cutting-edge drug design system that uses artificial intelligence (AI) to accelerate the drug development process. Standigm's AI-based platform, Standigm BEST, generates new compounds by exploring the latent chemical space with machine learning algorithms. In addition to saving money and time during the development phase, this method removes ambiguity from the drug discovery process. Standing BEST offers biological interpretations of the candidate treatments to assist researchers in identifying pathways and therapeutic patterns, ranking prospective targets, and continuing their drug design if they are recognized. The platform's power to evaluate biomedical literature offers researchers a thorough understanding of the body of information currently known in a given sector, which expedites the job of designing and developing new drugs. This enables them to create pharmaceuticals that efficiently reveal the characteristics they need and make well-informed choices. By presenting an unusual strategy for medication design, Standigm's AI-based platform helps to shorten the time and expense involved in studies on drugs. The pharmaceutical sector might be severely impacted by such inventive innovation, which could also lead to better patient results.

Cytoreason

An Israeli start-up called CytoReason provides creative approaches to the problem of drug discovery. The business analyzes vast volumes of biological data, including proteomics, genomics, and other omics data, using AI and ML algorithms. CytoReason's analysis of this enormous quantity of data enables data-driven target discovery, which aids in the identification of possible targets for medication development. Finding target molecules in the human body that medications can interact with is one of the first steps in the drug development process. However, this can be a challenging and time-consuming procedure because many human genes and proteins are still not fully understood. By utilizing its platform for evaluating multi-omic human clinical data, CytoReason resolves these issues. This enables the business to find gene and cell maps associated with illness states, giving scientists important information concerning possible targets for medication development and discovery. The platform speeds and improves the productiveness of the drug development process by supporting research and development activities at every stage of the process. CytoReason is transforming the drug development process, making it simpler to generate innovative treatments for a wide variety of ailments by utilizing AI and ML to evaluate vast volumes of biological data.

Genome biologics

Preclinical drug development is being greatly impacted by a German start-up called Genome Biologics. Genome Biologics is helping to expedite the medication development process by decreasing uncertainty in preclinical tests through automated sample screening for drug effects. GENIMPAS, the startup's platform, matches drug research pipelines and chemical substances databases with disease-relevant gene profiles via the use of machine learning and pattern recognition. This makes it possible to identify novel chemicals and use old ones to treat a variety of illnesses, such as cancer and cardiometabolic and cardiac conditions. The most recent patented solution from Genome Biologics is called GENISYST, which is a multiplexed disease modeling system for preclinical testing based on single-cell transgenics. Time and money may be saved by testing more than one goal at once using this creative strategy. In general, companies such as Genome Biologics offer useful instruments and solutions that contribute to the increased effectiveness and efficiency of preclinical drug discovery. Their contributions to the field may result in the creation of more potent remedies for a variety of problems.

Bullfrog AI

Bullfrog AI represents a US-based startup that hopes to use its very own artificial intelligence platform, bfLEAP, to transform the drug development process. The platform looks for linkages and correlations between medicines and patients by analyzing clinical trial data sets employing sophisticated data analysis techniques, including the use of natural language processing (NLP). Ultimately, bfLEAP aims to offer new perspectives on the connections and associations between interventions and patients. The ultimate objective of bfLEAP is to uncover new information on late-stage drug candidates, new pharmacological targets, synergistic drug combinations, and patient groups who might benefit substantially from a certain medication. Given the poor success rate of clinical studies and the need for instruments to increase accuracy and efficiency, this creative strategy is critical. In the arenas of artificial intelligence and pharmaceuticals, the use of natural language processing (NLP) to analyze large, complex amounts of data and extract valuable information is a fascinating finding with many potential applications. All things considered, BullFrog AI's bfLEAP platform serves as an illustration of the creative solutions that AI can provide to the pharmaceutical sector. The drug development process may be streamlined to enhance patient outcomes and make the trip from discovery to market quicker, with fewer mistakes, and more cost-effective through the use of modern data analytic tools.

Causaly

An AI start-up called Causaly is situated in the UK and concentrates on extracting causal relationships from biological literature. CEO Alexander Jarasch, CTO Julius Juettner, and COO Rodolfo Bellesi started the business in 2017. With the use of NLP and ML algorithms, Causaly's platform can determine the causal connections between biological concepts like drugs. They are genes, illnesses, and biological processes. Additionally, the platform can find new targets for pharmaceuticals and repurpose present drugs for use in novel contexts. Applications for the company's technology include pharmacovigilance, precision medicine, and drug research and development. To expedite drug development and detect possible adverse medicine responses, it is collaborating with several pharmaceutical firms, academic institutions, and government organizations [26].

Deep cure

A promising newcomer to the pharmaceutical sector, DeepCure leverages artificial intelligence (AI) to speed up the search for small-molecule medicines. Its lightning-fast detection of potential small molecules with required features is made possible by a mix of deep learning, cloud computing, and its proprietary database, MolDBTM. The risk and expense of subsequent phases of drug development are reduced when small compounds are optimized for significant pharmacokinetic functions, such as ADMET. In the pharmaceutical business, small molecules are essential and make up a sizable fraction of all medications available. Even though it is not too difficult to create them from recognized useful drug derivatives, occasionally their safety or effectiveness may be reduced by their interactions with prescription drugs. Here, the novel strategy of DeepCure is investigated, using machine learning algorithms to examine structural and chemical data from open sources and guarantee the high caliber of the treatments found. All things considered, DeepCure’ s emphasis on small-molecule medicines is a great asset to the pharmaceutical sector and may shorten the time and expense associated with drug research and development. The company's creative strategy and inventive use of technology bode well for the future of the field and its capacity to enhance patient outcomes [27].

How AI could bolster pharmaceutical adherence 

AI for smarter drug development

One of the most well-known examples of a supercomputer that has shown its AI capabilities outside of the lab is probably IBM Watson. Watson can not only take notes on exam questions for Jeopardy, but he can also read through several pages of exploratory restorative writing, identify linkages between prescriptions and other possible illnesses, and extract important information.

IBM announced the pharmaceutical behemoth Johnson & Johnson and rival Sanofi will work with IBM Watson's Discovery Advisor team a year ago. To create and assess different therapies, J&J may teach the supercomputer to read and comprehend experimental papers that incorporate clinical trial results. This may not seem very interesting, but it might have unimaginable consequences for the way pharmaceutical businesses operate. Comparable viability calculations. According to the IBM declaration, surgeons may find it beneficial to coordinate a drug with the appropriate patient arrangement to maximize adequacy and reduce symptoms. This would be different from the current manual procedure, which requires months to find evidence and information before an analysis.

Watson's use might effectively shorten the duration, which would expedite the disclosure process. According to reports, this was the first public statement from pharmaceutical companies recognizing the potential of a supercomputer and using it for forward-thinking research on medication development [28].

AI for Alzheimer’s patients

A program investigated the use of AI frameworks to assist and improve the autonomy and quality of life of people with Alzheimer's disease at the University of Washington's computer science department. These assisted decision-making platforms would replace a portion of an Alzheimer's patient's lost recollection and critical thinking faculties using artificial intelligence (AI) innovation. Scientists say that the motivation for this project is the desire to improve the well-being and individuality of individuals who are facing cognitive limitations as a result of aging and Alzheimer's disease [29].

AI for wearable health

How to use the data from Internet of Things (IoT) apps has been a problem because PCs quickly reached the limit of what was practical with all the people collected. Fortunately, machine learning frameworks have evolved to accommodate larger data sets. Microsoft's general director of personal devices, Zulfi Alam, explains in a post that their ingenious future algorithms will know enough about the customer and her biometrics in a consistent state to be able to recognize examples and opportunities to improve customer wellness.

An experimental group at the University of California, Los Angeles, suggests a stage for remote sensor systems-based well-being observation in a more traditional human services environment. The engineering of the stage is a system-powered framework that supports various wearable sensors and has general computing capabilities on board to carry out particular event detection, alerts, and system communications with multiple healthcare informatics services [30].

AI in pharmacy

Klopman investigated the structure-activity relationship (SAR) of natural atoms. The structure assessment program has been computerized and uses the KLN code, which is a straightforward coding system for atoms, to identify atom structures. From there, it further identifies, organizes, and takes apart biospheres, which are foundations that are measurably responsible for the natural behavior of the particles. The PCs in the computerized drugstore arrangement initially get electronic drug orders from the physicians and drug experts at UCSF. Following this, networks choose, package, and transport individual pills using mechanical technologies. Machines that accumulate the pieces onto a plastic ring with a barcode replicate this. All of the medications that a patient has to take within 12 hours are contained in a thin plastic ring. The computerized framework's ability to schedule sterile arrangements meant for chemotherapy along with filling intravascular syringes with suitable medicines is an additional capability.

The mechanical innovation's capabilities include the administration of injectable and oral medications, including toxic chemotherapy sedates. This has made it possible for the UCSF medical attendants and medication specialists to focus on direct patient anxiety while likewise working together with the physicians, encouraging them to make the most of their training.

Pharmaceutical applications 

By utilizing technological breakthroughs, the pharmaceutical sector may expedite discoveries. Artificial intelligence comes to mind as a recent breakthrough. Artificial intelligence (AI) has the potential to significantly improve decision-making by analyzing data and providing findings that save money, time, and human labor, thereby potentially saving lives.

Drug repositioning 

This study aims to determine the most effective molecular starting points for repurposing an established medicine or combination to investigate its potential for treating different ailments by analyzing its targets, mode of action, and genomic or proteomic fingerprint.

Alternative indication identification 

What unusual, encouraging uses exist for an individual class of inhibitors? By going over all the information available on indications and classifying them using published trials' quality, number, and applicability.

Epidemic outbreak prediction 

AI allows for the historical analysis of social media activity and epidemic outbreaks, as well as the very accurate prediction of the location and timing of epidemics.

• Contributing to the creation of new tools for patients, doctors, and
• Adjusting the duration of therapy.

Robotics in pharmacy

Robotics has a major role in dispensing systems.

• Safety and quality- It leads to a reduction in dispensing errors and releases staff from the dispensary for direct patient care support.
• Financial- 1. Reduced stock holding.

2. Improved stock rotation.
3. Reduction in Expired stock wastage.

• Process efficiencies- 1. Faster dispensing process to reduce patient waiting times.

2. Out of an hour’s activity.
3. Improved space utilization.

• Reliability: All medications must be monitored and traceable throughout manufacture, per FDA regulations. Pharmaceutical businesses can more easily meet these standards with the help of robots. Robots also reduce waste and accidents in a similar vein.
• Production: Robots boost throughput speed, which has an immediate effect on output! Robots can create more than a human workers since they can work continuously without taking breaks, vacations, or nights off.
• Lower danger of contamination: Taking individuals out of the screening process lowers the possibility of contamination and dropping samples while working with them in labs. These jobs are completed far more quickly and accurately by robots.
• Boost efficiency: When robots become more efficient, the cost of the medication itself becomes more competitive. People are not as efficient as machines when it comes to producing pharmaceuticals, especially when they are donning protective gear.

AI in healthcare 

AI systems must be taught using data produced by clinical procedures like screening, diagnosis, therapy, and so on before they can be used in healthcare applications.

A significant amount of the work on AI specifically examines data from diagnostic imaging, genetic testing, and electro diagnosis at the diagnosis stage.

Disease focus

Despite the growing body of research on AI in healthcare, the field mostly focuses on three illness categories: cancer, nervous system disorders, and cardiovascular diseases.

• Cancer: Using double-blind validation research, Somashekhare et al. showed that IBM Watson for Oncology would be a trustworthy AI system to aid in the detection of cancer.
• Neurology: Bouton et al. created an AI system to help quadriplegic patients regain control over their motions.
• Cardiology: Dilsizian and Siegel talked about how an AI system may be used to use cardiac imaging to identify heart problems. It is not entirely surprising that this illness is concentrated in this area.

AI in various techniques 

• Artificial Neural Network: This artificial intelligence approach is the most fascinating and is most frequently applied in the medical field. It is an effective method that uses a mathematical approach called non-linear mapping to activate biological brain networks. It stimulates the brain's neural networks' capacity for pattern recognition. Because ANNs can identify patterns in complicated sets of analytical data, they are particularly helpful for data analysis in pharmaceutical research because they can identify even non-linear associations in noisy data.

Its Applications -

• Drug modeling
• Dosage design
• Protein structure
• Function prediction
• Pharmacokinetic and pharmacodynamic modeling
• In-vitro and In- vivo correlation

AI in clinical practice 

Data collection, archiving, normalization, and tracking are important uses of AI in the healthcare industry. Deep genomics looks for mutations and connections to the disease by identifying patterns in massive databases of genetic data and medical records and developing a new class of computational tools that will be able to inform doctors about the consequences of therapeutic or natural genetic variation on a cell's DNA. In the realm of pharmaceutical research, clinical trials might take up to ten years and incur billions of dollars.

Discussion

Data digitalization in the pharmaceutical industry has increased dramatically in recent years. However, the complexity of acquiring, analyzing, and applying such knowledge to resolve complex healthcare problems also comes with digitalization. AI systems are more widely adopted because of their increased automation and capacity to handle large amounts of data.

AI technology includes a variety of cutting-edge instruments and networks that can simulate human intellect. It does not, however, pose a danger of totally replacing human intervention. Artificial intelligence (AI) technology creates software and systems that can independently assess, understand, and learn from the supplied data to make decisions for reaching certain objectives. Its applications in the pharmaceutical sector are constantly growing, as this paper illustrates.

Since an immense amount of data will be needed to train the algorithm in the future, this data must be freely accessible for the development of AI. To ensure reliable outcome prediction, the pharmaceutical sector may have to pay additional charges for access to data from many database suppliers. Additionally, the data must be of high quality and dependability. The lack of trained personnel to operate AI technology, small organizations' limited budgets, the unease of replacing humans and the resulting job losses, the uncertainty surrounding the data generated by AI, and the "black box" phenomenon, the term used to describe the methods by which AI technology draws its conclusions, are additional obstacles preventing the full-fledged adoption of AI in the pharmaceutical industry [31].

Over time, several jobs related to medication research, clinical trials, and sales will be automated. However, these tasks fall under the category of "narrow AI," meaning that the AI has to be trained on a huge amount of data to be appropriate for a specific activity. Thus, for the AI platform to be developed, implemented, and run successfully, human interaction is needed. But, considering that AI is already replacing monotonous tasks and freeing up human brains for the development of more complex insights and creative expression, the worry of unemployment may be unfounded. These AI technology features might improve research efficiency in the event of a public health emergency, but they may present ethical concerns, particularly when used in non-pandemic settings.

The pharmaceutical sector has to be made clear about the potential of artificial intelligence (AI) to solve issues once it is put into practice and what reasonable objectives may be met. It is possible to train proficient data scientists and software engineers to use every facet of AI technology if they have a solid understanding of AI technology, the pharmaceutical industry, and its R&D goals [32].

Conclusion and future aspects

The increasing interest in artificial neural networks (ANNs) has demonstrated significant potential in a range of pharmacological applications. This is already occurring in gene delivery and medicine. Peptides (CPPs) are frequently used to transport medications into cells. Recently, the effectiveness of CPP has been studied and analyzed using ANNs. In addition, this model offered thirteen important forecasts and assessments. Reusing medications is another benefit of this technology.

The majority of international healthcare businesses will employ AI by 2025, according to the most recent polls. Oncology and chronic illnesses will receive more funding. The majority of Americans die from chronic illnesses. Artificial intelligence is being utilized more and more to save expenses and enhance the treatment of chronic illnesses. Future chronic illnesses will be managed, such as diabetes, renal disease, cancer, and IPF. AI will thus have an impact on the advancement of medicine. AI ensures the best candidates are selected for the test and that patients may be quickly examined.

By reducing elements that might impede clinical research, a sizable test group is not necessary. AI is also useful for screening and diagnosing patients. AI is capable of extracting mRI images and mammograms from data. The development of medications will continue to be aided by AI and machine learning. Artificial intelligence (AI) is also entering the pharmaceutical and manufacturing systems.

To put it briefly, digitizing pharmaceutical research may greatly benefit from applying AI and machine learning. Machine learning is being applied quickly in pharmaceutical companies, demonstrating its versatility. In actuality, the kind of data and the dataset size might influence the method of learning. Therefore, it is task-specific. High-value AI applications can become routine with enough data.

Acknowledgement

I am grateful to Prof. Y.P. Sharma for encouraging me to learn about scientific data as well as provide inspiration for my research.

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