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Bree holds both a BSc and PhD in Genetics from the University of Liverpool. After completing her studies, she spent two years as a science writer at an agency. Eager to broaden her expertise, she joined Technology Networks as a science writer in 2024. In her current role, she is responsible for producing custom written content and contributing to the development of digital media.
Drug discovery has long been plagued by high costs, inefficiencies and high failure rates. Therefore, many companies are now adopting AI to accelerate innovation and improve outcomes across every phase of development.
AI is reshaping drug discovery and development by streamlining workflows, predicting molecular interactions and optimizing clinical trials, thereby reducing reliance on traditional, slower methods.
This infographic highlights how AI is revolutionizing drug discovery, showcasing its potential to lower costs, boost success rates and bring life-changing therapies to market faster.
Download this infographic to explore:
How AI enhances each stage of the drug discovery process
The role of AI in optimizing molecular properties and predicting clinical outcomes
The current use of AI in discovering molecules now progressing through clinical trials
How Is AI
Revolutionizing
Drug Discovery?
Drug discovery is costly, inefficient and often unsuccessful. Between
2000 and 2015, over 86% of drug candidates failed to meet their clinical
endpoints.1 As a result, many companies are now implementing AI to
.1
streamline the process, accelerating discovery, reducing costs and
increasing the likelihood of success at each stage of drug development.
2
This infographic explores how AI is transforming drug discovery, helping
companies cut costs, increase efficiency and boost success rates across
each stage of development – from target identification to clinical trials.
Target Identification
Data
What biomolecules can be
mining
a drug target?
Potential drug targets include a wide range of
biomolecules, such as receptors, enzymes, proteins,
genes and RNA. For a target to be considered
“druggable,” its activity must be modifiable by a
therapeutic agent. AI-based methods enhance
target identification by analyzing diverse datasets,
including published literature, patent records
and molecular databases for drug–disease, gene–
Published
Patent
disease and target–drug associations
.3
literature
records
Hit identification
QSAR
What compounds can bind
models
the target?
Virtual screening (VS) is a computational approach
that searches extensive libraries to identify “hit”
compounds – those most likely to bind to a drug
target. AI significantly enhances VS by improving
efficiency, reducing costs and identifying promising
candidates faster.4 It facilitates high-throughput
4
screening and can be used to develop advanced
pharmacophore and quantitative structure
High-throughput
Pharmacophore
activity
relationship (QSAR) models, which are
screening
models
essential for predicting interactions between
compounds and target proteins.
Lead optimization
Can we optimize candidates for
Predict off-target
effects
safety and efficacy?
Lead optimization focuses on refining hit
compounds by improving their ADMET (absorption,
distribution, metabolism, excretion and toxicity)
properties as well as the compound’s activity,
potency and selectivity. AI-driven models predict
how chemical modifications might enhance drug
efficacy, reduce toxicity and improve bioavailability.
By analyzing extensive datasets, AI algorithms
can identify structure-activity relationships (SAR),
Optimize molecular
optimize molecular features, and predict off
features
target
effects, helping researchers develop safer
and more effective drug candidates.
Preclinical testing
Improve predictions
about drug behaviour
Will the drug work in living
organisms?
Predicting drug responses is a critical step in the
drug development pipeline. AI-based techniques,
including similarity and feature-based machine
learning methods, can predict how the drug behaves
in the body, reducing the reliance on animal
testing and improving predictions about the
drug's success in human trials.5 Machine learning
5
models can also assess toxicity risks early,
Reduce reliance on
Assess toxicity risk
animal testing
early
enhancing safety profiles before clinical trials.
Clinical testing
Recruiting patients
Optimizing protocols
Can we streamline the testing
process?
Scientists are starting to use AI to manage clinical
trials, including the tasks of writing protocols,
recruiting patients and analyzing data.6 AI can
.6
help to design clinical trials by predicting patient
responses, stratifying participants based on
genetic or biomarker data and optimizing trial
protocols. AI-driven analytics can also monitor
real-time patient data to identify safety issues and
Stratifying patients
Analyze real-time
based on genetics and
improve trial outcomes.
patient data
biomarkets
Over the past five years, increasing numbers of drugs and vaccines have been
discovered with AI, many of which are now progressing through clinical trials
.7
Number of AI-discovered molecules in clinical trials
Launched
Phase III clinical trial
Phase II clinical trial
Phase I clinical trial
Number of AI-discovered molecules in clinical trials
AI-discovered targets
Other
AI-discovered small molecules
AI-discovered antibodies
AI-discovered vaccines
AI-discovered molecules
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
AI’s future in drug discovery
As AI continues to advance, its impacts on drug development are poised
to revolutionize every stage of the process, unlocking new treatments
and reshaping healthcare. With the inclusion of AI in the manufacturing of
pharmaceutical products, companies can achieve greater precision and
customization in drug production, tailoring medications to individual patient
needs. AI-driven technologies will not only speed up the time-to-market for
new therapies but also enhance product quality and improve the safety of
production processes.
References
1. Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates
Applications in Drug Discovery. Front Pharmacol. 2018;9. doi:10.3389/
and related parameters. Biostatistics. 2019;20(2):273-286. doi:10.1093/
fphar.2018.01275
biostatistics/kxx06
5. Qureshi R, Irfan M, Gondal TM, et al. AI in drug discovery and its clinical
2. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep
relevance. Heliyon. 2023;9(7). doi:10.1016/j.heliyon.2023.e17575
learning in drug discovery. Drug Discov Today. 2018;23(6):1241-1250.
6. Hutson M. How AI is being used to accelerate clinical trials. Nature.
doi:10.1016/j.drudis.2018.01.039
2024;627(8003):S2-S5. doi:10.1038/d41586-024-00753-x
3. Rehman AU, Li M, Wu B, et al. Role of Artificial Intelligence in
7. KP Jayatunga M, Ayers M, Bruens L, Jayanth D, Meier C. How
Revolutionizing Drug Discovery. Fundam Res. Published online May 9,
successful are AI-discovered drugs in clinical trials? A first analysis and
2024. doi:10.1016/j.fmre.2024.04.021
emerging lessons. Drug Discov Today. 2024;29(6):104009. doi:10.1016/j.
4. Neves BJ, Braga RC, Melo-Filho CC, Moreira-Filho JT, Muratov
drudis.2024.104009
EN, Andrade CH. QSAR-Based Virtual Screening: Advances and
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