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Vol 1 | Issue 2 | Jul - Sep 2022                                                                                        Indian J Pharm Drug Studies | 47 

Review Article  

The Era of Artificial Intelligence in Pharmaceutical Industries - A review 

Praveen Tahilani1, Hemant Swami2, Gaurav Goyanar2, Shivani Tiwari1 

From, 1 Sagar Institute of Research and Technology - Pharmacy, Bhopal, MP, 2 School of Pharmaceutical Science, SAGE University, 

Indore M.P. 

Corresponding to: Mr. Praveen Tahilani, Sagar Institute of Research and Technology- Pharmacy, Bhopal, M.P. Email: 

tahilanipraveen@gmail.com, Tel: 8770501549 

ABSTRACT 

As a growing sector, the Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry contributes 

in the drug discovery process, giving emphasis on how new technologies have improved effectiveness. As in the current scenario 

artificial intelligence including machine learning may be considered the future for a wide range of disciplines and industries specially 

the pharmaceutical industry. As we know today pharmaceutical industries producing a single approved drug cost the company 

millions with many years of rigorous testing prior to its approval, reducing costs and time is of high interest. The involvement of 

Artificial Intelligence will be useful to the pharmaceutical industry and also be of interest to anyone doing research in chemical 

biology, computational chemistry, medicinal chemistry and bioinformatics. 

Key words: Artificial Intelligence, Pharmaceutical, Machine Learning, Research, Chemistry 

rtificial intelligence (AI) is a branch of study that 

combines intelligent machine learning, particularly 

intelligent computer programmes that produce 

outcomes like human attention processes [1]. This procedure 

typically entails gathering data, creating effective mechanisms 

for using that data, illuminating precise or approximative 

conclusions, and making oneself modifications or adjustments 

[2]. AI is typically used to analyse machine learning to mimic 

human cognitive functions [2, 3].  

AI is used to conduct analyses that are more accurate and 

to get helpful interpretation [3]. According to this the 

development and invention of AI applications are frequently 

linked to the concern about the threat of unemployment. 

However, practically all developments in the use of AI 

technology are being applauded because of the industry's 

massive reliance on its effectiveness. The practical uses of AI 

technology in numerous technological and scientific 

disciplines have recently made it a highly vital component of 

industry [3, 4].  

The emerging movement to accept AI applications in 

pharmacy, including drug discovery, formulation creation for 

drug administration, and other healthcare applications, has 

already moved from hype to hope [5, 6]. Predicting in vivo 

reactions, therapeutic drug pharmacokinetic characteristics, 

appropriate dose, etc. is also made possible by the use of AI 

models [2, 7]. According to the importance of 

pharmacokinetic prediction of drugs, the uses of in silico 

models facilitate their effectiveness and inexpensiveness in the 

drug research [8].  

Artificial intelligence (AI) has become more prevalent in a 

number of societal fields, most notably the pharmaceutical 

industry. In this review, we focus on how AI is being used in a 

variety of pharmaceutical industry fields, such as drug discovery 

and development, drug repurposing, increasing 

pharmaceutical productivity, and clinical trials, among others. 

This use of AI lessens workload of human workers while also 

achieving goals quickly. We also talk about how various AI 

tools and methodologies interact, current problems and 

solutions, and the potential applications of AI in the 

pharmaceutical sector. The pharmaceutical industry has 

dramatically increased its data digitization during the last few 

years. The difficulty of gathering, examining, and applying 

that knowledge to address challenging healthcare situations is 

a challenge that comes along with digitalization [9].  

Because AI can handle massive amounts of data with 

improved automation, this encourages its usage [10]. 

Technology-based artificial intelligence (AI) systems can 

replicate human intelligence by using a variety of cutting-edge 

tools and networks. However, it does not pose a danger to 

totally replace human physical presence [11, 12]. AI makes 

use of hardware and software that can analyse and learn from 

input data to make independent judgements for achieving 

predetermined goals. As this review describes, its uses in the 

pharmaceutical industry are constantly expanding. According 

to the McKinsey Global Institute, the rapid advances in AI-

A 

mailto:tahilanipraveen@gmail.com


Tahilani et al                                                                                      Artificial Intelligence in Pharmaceutical Industries 

Vol 1 | Issue 2 | Jul - Sep 2022                                                                                        Indian J Pharm Drug Studies | 48 

guided automation will be likely to completely change the 

work culture of society [13, 14]. 

AI: Networks and Tools: AI encompasses a number of 

approach fields, including machine learning as its core 

paradigm as well as reasoning, knowledge representation, and 

solution search (ML). In machine learning (ML), algorithms 

are used to find patterns in a set of data that has been further 

categorised. Deep learning (DL), a branch of machine learning 

that uses artificial neural networks (ANNs). These are a 

collection of intricately connected computing components that 

simulate the electrical impulse transmission in the human 

brain by using "perceptons" that are similar to biological 

human neurons [15]. Every node in an ANN receives a 

different input, and they all work together or alone to solve 

issues by converting inputs to outputs using algorithms [16]. 

ANNs involve various types, including multilayer perceptron 

(MLP) networks, recurrent neural networks (RNNs), and 

convolutional neural networks (CNNs), which utilize either 

supervised or unsupervised training procedures [17, 18]. 

Classification of AI 

According to their ability, AI can be categorized as 

i) Artificial Narrow Intelligence (ANI) or Weak AI: It 

performs a narrow range task, i.e., facial identification, 

steering a car, practicing chess, traffic signalling, etc. 

ii) Artificial General Intelligence (AGI) or Strong AI: It 

performs all the things as humans and also known as human 

level AI. It can simplify human intellectual abilities and able 

to do unfamiliar task.  

iii) Artificial Super Intelligence (ASI): It is smarter than 

humans and has much more activity than humans drawing, 

mathematics, space, etc.  

According to their presence and not yet present, AI can be 

classified as follows 

 i) Type 1: It is used for narrow purpose applications, which 

cannot use past experiences as it has no memory system. It is 

known as reactive machine. There are some examples of this 

memory, such as a IBM chess program, which can recognize 

the checkers on the chess playing board and capable of 

making predictions.  

ii) Type 2: It has limited memory system, which can apply the 

previous experiences for solving different problems. In 

automatic vehicles, this system is capable of making decisions 

there are some recorded observations, which are used to 

record further actions, but these records are not stored 

permanently.  

iii) Type 3: It is based upon “Theory of Mind”. It means that 

the decisions that human beings make are impinged by their 

individual thinking, intentions and desires. This system is non-

existing AI.  

iv) Type 4: It has self-awareness, i.e., the sense of self and 

consciousness. This system is also non-existing AI. 

Artificial Intelligence and Robotics: Robotics and artificial 

intelligence share a shared origin and a long history of 

interaction and scholarly debate. It might be argued that not 

all machines are robots, and that artificial intelligence likewise 

has concerns for virtual agents. Robots are produced as 

hardware and artificial intelligence is a hypothesis. The two 

are related because a software agent that controls the robot 

examines data from these sensors, decides what to do next, 

and then directs the actions to be taken in the real enviro-

nment. It has numerous robotics applications [20]. Patients 

will also look into potential drug options as they become more 

involved in their healthcare decision. Through target audience 

marketing, pharmaceutical companies can further assure the 

right information is presented at the right time to facilitate 

informed patent and provider discussions [19].  

 “It is time for Connected Pharma”: However, progress is 

far from uniform and progress is likely to be “lumpy” at best. 

AI technology is well on its way to becoming ubiquitous and 

has huge scope, enhancing technology at many levels, leading 

to much better, faster patient outcomes. 

Pharmaceutical Automation: Assisted by artificial 

intelligence Industrialization produced automation because it 

was necessary to boost output, produce items of consistently 

high quality, and free people from dangerous and taxing tasks. 

Technology advancements today provide the fundamental 

foundation of automation. The majority of Pharma players are 

aware of the advantages of implementing new technology, but 

there is still a persistent and alarming gap between strategy 

and an organization's capacity to implement a workable data 

analytics solution [21]. 

The adoption of AI allows for learning from real - time 

data.  

 Identifying the right candidates for clinical trials.  

 Processing real time patient feedback.  

 Integrating data exchanges with partners.  

 Distributors and caregivers.  

There are just few examples on how to improve drug 

discovery outcomes, while aligning operational efficiencies to 

deliver better care to the patients, often getting the right 

medication to the right patient at right time is really about 

getting right information in front of healthcare provider. 

Armed with complete real-time drug insights, doctors are able 

to choose right prescription for the best possible outcome. 

Automation applications continue to grow with enabling 

technologies such as [22]:  

1. Wireless  

2. Nanotechnology  



Tahilani et al                                                                                      Artificial Intelligence in Pharmaceutical Industries 

Vol 1 | Issue 2 | Jul - Sep 2022                                                                                        Indian J Pharm Drug Studies | 49 

3. Advance storage and memory  

4. Sensors and analyzers   

5. Advance software algorithms  

6. Artificial intelligence. 

AI in Advancing Pharmaceutical Drug Devlopment: The 

subsequent inclusion of a novel therapeutic molecule into an 

appropriate dosage form with the requisite delivery properties 

is necessary. The traditional method of trial and error can be 

replaced in this area by AI [23]. With the use of QSPR, a 

variety of computational methods can be used to overcome 

challenges with stability, dissolution, porosity, and other 

aspects of formulation design [24]. Decision-support tools 

operate through a feedback mechanism to monitor the entire 

process and sporadically adjust it [25]. They employ rule-

based systems to choose the type, nature, and quantity of the 

excipients based on the physicochemical properties of the 

medicine. Based on the input parameters, the Model Expert 

System (MES) decides and gives suggestions for formulation 

development. ANN, in contrast, ensures hassle-free 

formulation development by using backpropagation learning 

to link formulation parameters to the intended response, which 

is jointly regulated by the control module [26]. The influence 

of the powder's flow behaviour on the die-filling and tablet 

compression process has been studied using a variety of 

mathematical tools, including computational fluid dynamics 

(CFD), discrete element modelling (DEM), and the Finite 

Element Method [27, 28]. The effect of tablet geometry on its 

disintegration profile can also be studied using CFD [29]. The 

quick manufacture of pharmaceutical items may benefit 

greatly from the integration of these mathematical models 

with AI. 

AI in Pharmaceutical Marketing: Modern manufacturing 

systems are attempting to impart human knowledge to robots 

as a result of the growing complexity of production processes, 

as well as the growing desire for efficiency and greater 

product quality [30]. The pharmaceutical business may benefit 

from the application of AI in manufacturing. Utilizing the 

automation of many pharmaceutical activities, tools like CFD 

use Reynolds-Averaged Navier-Stokes solvers technology to 

examine the effects of agitation and stress levels in various 

pieces of equipment (such stirred tanks). Similar methods, 

including big eddy simulations and direct numerical 

simulations, use sophisticated techniques to address 

challenging flow problems in manufacturing [31]. The 

innovative Chapter platform, which uses a scripting language 

called Chemical Assembly and several chemical codes, aids 

digital automation for the synthesis and manufacture of 

molecules [32]. Sildenafil, diphenhydramine hydrochloride, 

and rufinamide have all been successfully manufactured using 

this method, and the yield and purity are noticeably similar to 

those obtained through manual synthesis [33]. AI technology 

can effectively complete granulation in granulators with 

capacities ranging from 25 to 600 l [34]. Neuro-fuzzy logic 

and technology were used to correlate key factors with their 

answers. In order to anticipate the proportion of granulation 

fluid to be supplied, the necessary speed, and the diameter of 

the impeller in both geometrically identical and dissimilar 

granulators, they developed a polynomial equation [35]. 

AI in Quality Control and Quality Assurance: A balance of 

different factors must be achieved throughout the production 

of the desired product from raw materials [36]. It takes human 

intervention to maintain batch-to-batch consistency and 

conduct quality control testing on the products. This illustrates 

the need for AI deployment at this time and may not be the 

optimal strategy in every situation [37]. By implementing a 

"Quality by Design" approach, the FDA modified Current 

Good Manufacturing Practices (cGMP) in order to better 

understand the crucial process and precise standards that 

determine the ultimate quality of the pharmaceutical product 

[38]. 

AI in Clinical Filed: Clinical trials take about 6-7 years to 

complete and include a substantial financial outlay in order to 

determine the safety and effectiveness of a medicinal product 

in people for a specific illness condition. Only one out of 

every ten compounds that undergo these trials, however, 

receive successful clearance, which represents a significant 

loss for the industry [39]. These failures may be the result of 

bad infrastructure, poor technical requirements, and poor 

patient selection. With the use of AI, these problems can be 

minimised thanks to the abundance of digital medical data that 

is currently available [40]. 

CONCLUSION 

As a result of the AI technological approaches' belief that 

humans can imagine knowledge, solve problems, and make 

decisions, there has been an increase in interest in using AI 

technology for analysing and interpreting some critical areas 

of pharmacy, such as drug discovery, dosage form design, 

poly pharmacology, hospital pharmacy, etc. It has been found 

to be beneficial to use automated workflows and databases for 

efficient studies that apply AI techniques. The construction of 

novel hypotheses, strategies, predictions, and assessments of 

many connected elements can be done with the ease of less 

time consumption and affordability thanks to the usage of AI 

technologies. 

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How to cite this article: Tahilani & Swami. The Era of 

Artificial Intelligence in Pharmaceutical Industries- A 

review Indian J Pharm Drug Studies. 2022: 1(2); 47-50. 

Funding: None                   Conflict of Interest: None Stated 

 


