The Impact of Artificial Intelligence on Pharmacy Education, Research and Practice

Prajakta A. Kakade¹, Shivkumar M. Sontakke², Avinash H. Hosmani³, Indrajeet D. Gonjari⁴

¹Research Scholar, Department of Pharmaceutics, Government College of Pharmacy, Karad.

²Research Scholar, Department of Pharmaceutics, Government College of Pharmacy, Karad.

³Associate Professor, Department of Pharmaceutics, Government College of Pharmacy, Karad.

⁴Associate Professor, Department of Pharmaceutics, Government College of Pharmacy, Karad.

*Corresponding Author E-mail: prajaktakakade4319@gmail.com

ABSTRACT:

Artificial intelligence (AI) is transforming the present drug development and design method by tackling the obstacles addressed at each stage. AI improves the effectiveness of processes greatly by improving accuracy, reducing time and cost, using high-performance algorithms, and enabling computer-aided drug design (CADD). Efficient drug screening strategies are critical for discovering possible hit compounds among enormous amounts of data in compound repositories. The use of AI in drug development, comprising the screening of hit compounds as well as lead molecules, has shown to be more successful than traditional in vitro screening methods. This article examines advances in drug screening approaches made by AI-enhanced usage, machine learning (ML), along deep learning (DL) algorithms. It concentrates on AI applications for drug development, including screening methodologies and lead optimization approaches including quantitative structure-activity relationship (QSAR) modeling, pharmacophore modeling, de novo drug development, along high-throughput virtual screening. The discussion includes valuable insights into several parts of the drug screening process, focusing on the role of AI-based tools, pipelines, and case studies in reducing the intricacies of drug development.

KEYWORDS: Machine learning, Hit compounds, Drug discovery, Artificial intelligence, Pharmacophore modelling, Neural network.

INTRODUCTION:

Artificial intelligence (AI) is an area of computational science that involves tackling issues using symbolic programming. AI has significantly enhanced disease assessment. Disease diagnosis has become critical to developing effective medicines along with guaranteeing the health of patients. AI is quickly finding its way toward the field of healthcare. AI has been seen as "a key assisting role in the attempt to cure and prevent" the spread of the virus, and it may "contribute to an outcome emerging quicker than we might have normally" in the biotech area. AI is acknowledged as playing a critical helping role in the attempts to attack and manage the virus, perhaps quickening the discovery of remedies in the biotech industry¹. Drug research, formulation, development, and various other healthcare solutions belong to the developing endeavors in pharmacy that use AI technology. The current trend has already evolved from enthusiasm to optimism. The employment of AI models allows for the prediction of in vivo reactions, therapeutic pharmacokinetic characteristics, optimal dose, and so on². According to the importance of pharmacokinetic modeling in the study of drugs, the use of in silico models improves the medicine's efficacy and pricing. AI developments are divided into two groups. The first consists of standard computing technologies, which includes expert systems, that can simulate human experiences and draw judgments. From fundamental concepts, like expert systems³.

Figure 1: Advantages and Disadvantages of Artificial Intelligence in Pharmacy

1. AI Overview: AI (which is also referred to as machine intelligence) is sometimes misunderstanding as being used equally with robotics as well as automation. While robotics is merely the design of machines capable of doing challenging routine tasks, AI is the display of human-like behaviors or consciousness by any technological device or machine⁴. Traditionally, robots weren't intended to have these "intelligent capabilities," despite the fact that they can move or carry items autonomously using a predefined software along with surface detectors in a procedure referred to as automation. AI, in essence, is an area of computer science that focuses on the development of intelligent computers capable of doing tasks normally involving humans⁵. AI is widely used to create digital computers as well as computer-controlled robots capable of independently performing intellectual along with cognitive human-like functions. Learning, thinking, problem solving, understanding, and language are all examples of intellectual and cognitive procedures. The type of AI now in use is known as narrow AI or weak AI since it is solely meant to handle certain tasks such as internet search, facial as well as recognition of voices, controlling along with driving automobiles, and many more. Fortunately, the AI community's long-term objective is to create computers that can surpass humans across all cognitive activities. The general AI, also known as Strong AI (ADI), will create robots capable of doing all human cognitive functions⁶.

Simply described, artificial intelligence (AI) is the capability of robots and computers to act, think, behave, as well as function like humans. Representatives of AI-controlled systems comprise Apple's Siri (in iPhone)⁷, Amazon's Alexa⁸, and self-driving cars from Google, Mercedes, BMW, as well as Tesla just to name a few⁹. The heart of AI can be Information Engineering, in which robots are built with access to vast amounts of data and information about the human environment, allowing them to emulate human behavior. Machine Learning is additional sort of artificial intelligence that uses statistical models as well as algorithms to increase the precision of software programs in predicting events without being explicitly programmed. It was founded on the premise that robots may acquire knowledge from data, recognize issues, and make choices using little human assistance or interaction. The uses of machine learning includes self-driving Google vehicles, fraud detection, as well as online recommendation offerings such as those on Amazon as well as Netflix¹⁰.

2. History of AI:

Upon contributing an important part in identifying the domain devoted to the production of intelligent machines, John Mc Carthy, an American computer scientist pioneer as well as inventor, was named the "Father of Artificial Intelligence."¹¹.

Table 1: History of Artificial Intelligence (AI)

Year	Proposed Work	References
1943	Warren McCulloch along Walter Pits created a model of artificial neurons.	11
1949	Donald Hebb: Modifying the connection strength between neuron	12
1950	Alan Turing: Computing machinery along with intelligence	13
1955	Allen Newell and Herbert A. Simon: First artificial intelligence program which was named as “Logic theorist”	14
1956	John McCarthy: AI coined as an academic field	15
1966	Joseph Weizenbaum: First Chabot that termed as ELIZA	16
1972	WABOT-1 was the first intelligent humanoid robot created in Japan.	17
1974- 1980	First AI Winner	17
1980-1987	Stanford University hosted the American Association for Artificial Intelligence's first national symposium.	17
1987-1993	Second AI Winner	17
1997	IBM Deep Blue: The first computer to defeat a world chess champ.	16,17
2002	AI in Home: Roomba	16
2011	IBM‟s Watson: Wins quiz show	15
2012	Google has launched an Android app feature “Google now	17
2014	Chatbot Eugene Goostman: Won a competition in the infamous “Turing test”	14,17
2015	Amazon Echo	17
2016	In this year, the Go Champion Lee Sedol was defeated by Google DeepMind, software AlphaGo.	17

3. AI Classification:

Figure 2: Classification of AI in two different ways^18,19

3. 1 Classification based on Calibre:

1. Weak Intelligence or Artificial Narrow Intelligence (ANI): It is also known as Artificial Narrow Intelligence. This system is designed and trained to perform a narrow task, such as facial recognition, driving a car, playing chess, and traffic signaling. E.g.: Apple SIRI virtual personal assistance, tagging in social media^20,21.

2. Artificial General Intelligence (AGI) or Strong AI: It is also called Human-Level AI. It can simplify human intellectual abilities. Due to this, when it is exposed to an unfamiliar task, it can find the solution. AGI can perform all the things as humans^{20, 21}.

3. Artificial Super Intelligence (ASI): It is brainpower, which is more active than smart humans in drawing, mathematics, space, etc. in every field from science to art. It ranges from the computer just little than the human to a trillion times smarter than humans^20,21.

3.2 Classification Based on Presence: Arend Hintze, an AI scientist classified the AI technology based on its presence and not yet present. They are as follows:

1. Type A: This type of AI system is known as “Reactive Machine”. e.g., Deep Blue, the IBM chess program which hit the chess champion, Garry Kasparov, in the 1990s. It can identify checkers on the chessboard and can make predictions; it does not have the memory to use past experiences. It was designed for narrow purposes use and is not useful in other situations. Another example is Google's Alpha Go¹⁷.

2. Type B: This type of AI system is known as “Limited Memory System”. This system can use past experiences for present and future problems. In autonomous vehicles, some of the decision-making functions are designed by this method only. The recorded observations are used to record the actions happening in the future, such as changing the lanes by car. The observations are not in the memory permanently¹⁷.

3. Type C: This type of AI system is known as “Theory of Mind”. That means that all humans have their thinking, intentions, and desires which impact the decisions they make. This is a non-exist AI¹⁷.

4. Type D: This type of AI system is known as “Self- awareness.” The AI systems have a sense of self and consciousness. If the machine has self- awareness, it understands the condition and uses the ideas present in others brains. This is a non- existing AI¹⁷.

4. Neural networks and ANNs:

Figure 3: The classes of neural networks ^{22, 23}

5. Application of AI in Pharmacy:

Figure 4: Applications of AI

5.1 Drug Discovery:

Pharma companies all over the world are using cutting-edge ML Algorithms and AI- powered tools to speed up the drug discovery process. These intelligence technologies can be utilized to address issues related to complex biological networks because they are made to find nuanced patterns in vast datasets²⁴.

Figure 5: Drug development and discovery

5.2 Drug Development:

The application of AI has the potential to advance RandD. AI is capable of anything, from creating and finding new compounds to target-based medication validation and discoveries²⁵.

5.3 Diagnosis:

Large volumes of patient healthcare data may be collected, processed, and analyzed by doctors using cutting-edge machine learning systems. Sensitive patient data is being safely stored in the cloud or other centralized storage systems by healthcare providers all over the world utilizing ML technology.^26-28

5.4 Disease Prevention and Patient safety:

Pharma Companies can use AI to develop cures for both known diseases like Alzheimer’s and Parkinson’s and rare diseases. Generally, pharmaceutical companies do not spend their time and resources on finding treatments for rare diseases since the ROI is very low compared to the time and cost it takes to develop drugs for treating rare diseases²⁹. The research by Sharma (2024) examines how artificial intelligence (AI) is changing nursing. It was discovered that the integration of Natural Language Processing (NLP), Clinical Decision Support Systems (CDSS), and predictive analytics contributes to better patient care, more efficient workflow, and a redefined role for nurses³⁰. According to Londhe et al. (2024), the WHO Drug Monitoring initiative develops proactive drug and patient safety by enabling monitoring and identifying adverse medication responses that were previously unknown³¹.

5.5 Epidemic Prediction:

AI and ML are already used by many pharma companies and healthcare providers to monitor and forecast epidemic outbreaks across the globe. These technologies feed on the data gathered from disparate sources in the web, study the connection of various geological, environmental and biological factors on the health of the population of different geographical locations, and try to connect the dots between these factors and previous epidemic outbreaks. Such AI/ML models become especially useful for underdeveloped economies that lack the medical infrastructure and financial framework to deal with an epidemic outbreak^32,33.

5.6 AI in Science and Research:

AI is making lots of progress in the scientific sector. Artificial intelligence can handle large quantities of data and processes it quicker than human minds. This makes it perfect for research where the sources contain high data volumes. AI is already making breakthroughs in this field^34-36.

5.7 AI in Product management:

Businesses are adopting "Web Crawlers," one of the cutting-edge AI platforms, to gain marketing value in the online market and contribute to becoming a key player. Businesses are attempting to improve their websites over those of their rivals and provide temporary incentive programs, which impacts their market share and increases their popularity^37,38.

5.8 Data Analysis:

Artificial Intelligence and Data Analytics is the power to analyze and learn about large amounts of data from multiple sources and detect patterns to make future trend predictions. Business and industry benefits from predictive analytics to make decisions about production, marketing and development³⁹.

5.9 AI in Polypharmacology:

Now a day, ‘one-disease-multiple-targets’ concept governs over the ‘one-disease-one -targets’ concept for the advanced realization of pathological process in various disorders at their molecular basis. The phenomenon of ‘one-disease-multiple-targets’ is known as polypharmacology. There are numerous and useful databases, for examples, PubChem, KEGG, ChEMBL, ZINC, STITCH, Ligand Expo, PDB, Drug bank, Supertarget, Binding DB, etc, which are accessible for the accomplishment of a variety of important and useful information related to the structure of crystals, chemical features, biological properties, molecular pathways, binding affnities, disease concern, drug targets, etc. AI also helps to discover the databases to sketch polypharmacological molecules/agents⁴⁰.

5. 10 Clinical Research:

Big data and AI technologies are complimentary as AI can help to synthesize and analyses ever-expanding data.^41,42

6. AI Applied in Top Companies in the Word:

Table 2: The application of AI in the pharmaceutical and biotech industries.

Company	Application	References
Pfizer	Immune Oncology.	43
Roche	Diabetic Macular edema.	44
Novartis	Decode Cancer Pathology Images	45
Johnson Johnson	Stroke Related Death, Skin	43
Scanner Merck and Co MSD	Emphasis On Diabetic at Cancer Prevention	44
Sanofi	Drug repurposing identifies new uses of some of its Clinical strength molecule for genetic Disease	45
Glaxo Smith Kline	Drug Discovery has Artificial Intelligence unit, in-silico drug discovery unit.	43, 44
Amgen	Precision medicine in GNS health care medical research	43, 44
Gilead Sciences	Drug Discovery in April 2019	43, 44, 45

7. CONCLUSION:

During past few years, a considerable amount of increasing interest towards the uses of AI technology has been identifed for analyzing as well as interpreting some important fields of pharmacy like drug discovery, dosage form designing, polypharmacology, hospital pharmacy, etc., as the AI technological approaches believe like human beings imagining knowledge, cracking problems and decision making. The uses of automated workflows’ and databases for the effective analyses employing AI approaches have been proved useful. As a result of the uses of AI approaches, the designing of the new hypotheses, strategies, prediction and analyses of various associated factors can easily be done with the facility of less time consumption and inexpensiveness.

8. ABBREVIATIONS:

AI- Artificial Intelligence

CADD- Computer- aided drug design

ML- Machine Learning

DL- Deep Learning

QSAR- Quantitative structure- activity relationship

ANI- Artificial Narrow Intelligence

AGI- Artificial General Intelligence

ASI- Artificial Super Intelligence

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Received on 26.12.2024      Revised on 12.03.2025

Accepted on 24.04.2025      Published on 10.07.2025

Available online from July 17, 2025

Asian J. Pharm. Res. 2025; 15(3):327-332.

DOI: 10.52711/2231-5691.2025.00051

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