This document discusses how artificial intelligence is changing drug discovery and development. It outlines several ways AI can help, including better understanding drug targets and their properties, predicting drug-target interactions, aiding drug repurposing efforts, improving clinical trials through areas like patient recruitment and risk prediction, and enhancing pharmacovigilance. The current status sees AI being applied in these areas, but challenges remain around issues like disease complexity and data management at large scales. AI offers advantages like improved quality, cost-effectiveness, timeliness, and transparent data sharing.

1. Artificial Intelligence in
Drug Discovery &
Development
Dr. Manu Kumar Shetty
Associate Professor
Department of Pharmacology
Maulana Azad Medical College
New Delhi

2. AI is changing drug discovery
Time 40min
Why we need AI
What are AI Models
Advantages
Present status &
Challenges

3. AI/ML in Drug development,
Need?
“ Evolution is not a force but a process “
High attrition rate
Increased expenditure
Increase volume of data
Increase global regulatory requirement
Challenge in timely processing

4. Next Generation Drug
Development
“We’re really at the cusp of delivering huge improvements in drug discovery
and development”
Shift from traditional model to Next-Gen
automated and intelligent model
Next Generation
Drug Development
AI/ML/DL
NLP CNN
Big Data
Analytics

6. Role of AI in Drug-Discovery
Better compounds going into clinical
trials
Better target understanding
Better conductance of trials
Better Post Marketing surveillance

7. Drug Develop
1. Target protein properties
2. Ligand/drug properties
3. Target-ligand interaction
4. Drug repurposing
5. Clinical trails
6. Pharmacovigilance

8. 1) Target protein
understanding
The structure of the target protein is vital
to design the drug molecule.

10. 2. Drug/Ligand properties
prediction
Quantitative structure–activity
relationship (QSAR) -predict
physicochemical properties and
biological activities based on their
known chemical structure

11. Ligand property prediction

13. 3) Drug- Target interaction
(DTI)
Bioassay
Docking Simulation
Machine Learning methods

15. Drug Target Databases

18. 4). Drug Repurposing
Drug repositioning, drug retasking, drug
reprofiling, drug rescuing, drug
recycling, drug redirection, and
therapeutic switching
Process of identifying new therapeutic
use(s) for old/existing/available drugs
Highly efficient, time saving, low-cost
and minimum risk of failure, increases
the success rate

20. 5). Clinical Trial
Half of time and investment
86% of all trials do not meet enrolment
timelines
1/3rd of all Phase III trials fail owing to
enrolment problems
Patient recruitment takes up 1/3rd of
duration
A 32% failure rate because of patient
recruitment problems

21. AI models used to enhance
patient cohort selection
By reducing population heterogeneity
Prognostic enrichment
Predictive enrichment
Electronic phenotyping

22. AI techniques used to automatically
analyze EMR and clinical trial eligibility
databases, find matches between
specific patients and recruiting trials,
and recommend these matches to
doctors and patients
Predict the risk of dropout for a
specific patient

23. 6). Pharmacovigilance
Insertion of structured and unstructured content: NLP
and ML are used to extract ICSR information in a
regulatory compliant manner
AI for decision-making: AI may play an important role
in predicting the new ADR

24. NLP Example 1 - input

25. Output -
ALT2MEDCODING

26. NLP Example 2 - input

27. Normalize drug names and
dosage units with Spark NLP

30. Advantages
Improve the quality and accuracy
Cost-effective
Timely manner
Can handle diverse types data formats
Transparent data sharing with
regulators, prescribers
Prediction of new ADR

31. Present status

34. Challenges
Not a monocausal diseases
Significant data challenges, such as the
scale, growth, diversity, and uncertainty
of the data.

35. Thank you