The document discusses the significant role of artificial intelligence (AI) in the pharmaceutical sector, particularly in drug discovery and development. It highlights the use of AI technologies, such as deep neural networks, for tasks like drug repurposing, improving productivity, and personalized treatments, while also noting its application in medical record management and patient care. The future of automated drug discovery is emphasized, alongside the current advancements and challenges faced by AI in this field.

1. Presented by :
Nikita Savita
M.Pharm 3rd semester
Pharmacognosy dept.
Guided by :
Mrs. Manju Vyas
Associate Professor
DIPSAR, New Delhi

2. ARTICLE REFERRED

3.  Introduction
 Use of AI in pharmaceutical sector
 AI: things to know
 AI: networks and tools
 Overview of AI approaches
 Conclusion

4. Over the past few years, there has been a drastic increase in
data digitalization in the pharmaceutical sector.
Artificial Intelligence (AI) plays a pivotal role in drug
discovery.
In particular artificial neural networks such as deep neural
networks or recurrent networks drive this area.

5.  Drug discovery and development
 Drug repurposing
 Improving pharmaceutical productivity
 Activity predictions like :
1) Physicochemical properties
2) ADMET properties
 QSAR
 Clinical trials, etc.

6.  AI is being introduced in the medical field to keep
a medical record in digital format and conduct
patient checkup using smart technologies.
 It provides solutions, especially in targeted
treatments, uniquely composed drugs and
personalized therapies.
 AI is an innovative technology that helps to guide
the surgeon during medication, treatment and
operation.
 The main application of this technology is for
better decision-making for complicated cases.

7.  It can also help to track, detect, investigate and
control the infection in the hospital.
 This technology develops and optimizes online
patient appointment platforms.
 Assist in decision making.
 Determine the right therapy for a patient, including
personalized medicines.
 Manage the clinical data generated and use it for
future drug development.
Thus, AI helps in reducing the human workload as well
as achieving targets in a short period.

8. One tool developed is International Business Machine
(IBM) Watson supercomputer (IBM, New York, USA) in
February 14, 2011.
It was designed to assist in the
 analysis of a patient’s medical information
 its correlation with a vast database
 resulting in suggesting treatment strategies for
cancer.
This system can also be used for the rapid detection of
diseases. This was demonstrated by its ability to detect
breast cancer [14,15].

10. AI in drug development

11. Comparison of Conventional drug discovery with
Artificial Intelligence

12. Artificial intelligence (AI) empowered drug
repurposing

14.  AI is a technology based system involving various
advanced tools and networks that can mimic
human intelligence.
 Artificial intelligence is considered as intelligence
demonstrated by machines.
 AI can handle large volumes of data with
enhanced automation [2].

15.  AI involves several method domains, such as
reasoning, knowledge representation, solution
search, and among them, a fundamental paradigm
of machine learning (ML).
 ML uses algorithms that can recognize patterns
within a set of data that has been further classified.
 A subfield of the ML is deep learning (DL), which
engages artificial neural networks (ANNs).

16.  In particular, artificial neural networks, such as
deep neural networks (DNN) or recurrent neural
networks (RNN) drive the evolution of artificial
intelligence.
 In pharmaceutical research, novel artificial
intelligence technologies received wide interest.
 AI applications for early drug discovery has
been widely increased.

17. Method domains of artificial
intelligence (AI)

18.  Artificial intelligence has received much attention
recently and also has entered the field of drug
discovery successfully.
 Many machine learning methods, such as QSAR
methods, SVMs (Support vector machines) are
well-established in the drug discovery process.
 The applicability of AI including physicochemical
properties as well as biological activities, toxicity
etc.

19.  The application of AI for drug discovery benefits
strongly from open source implementations,
which provide access to software libraries.
 Frequently used open source libraries are :

21.  With progress in these different areas, we can
expect more and more automated drug discovery
done by computers.
 Large progress in robotics will accelerate this
development.
 Nevertheless, artificial intelligence is far from
being perfect.

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