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1EMERGING TECHNOLOGIES: IMPLEMENTING AI, ML, AND DL TO DRIVE DRUG DISCOVERY
Emerging Technologies: Implementing AI, ML, and DL to Drive Drug DiscoveryNew technologies are playing an increasingly important role in bio-pharma research.
2EMERGING TECHNOLOGIES: IMPLEMENTING AI, ML, AND DL TO DRIVE DRUG DISCOVERY
Drug discovery is an incredibly costly and time-consuming process. There are millions of potential compounds out there, and researchers now have unlimited possibilities for de novo drug design.
Qualifying enormous numbers of possible drug candidates requires
computing infrastructure that only the most sophisticated bio-pharma
research labs possess. Among those that do, there are even fewer that
have integrated efficient, automated strategies for using these resources
in an optimal way.
Computational-aided design is nothing new for bio-pharma researchers,
but today’s cutting-edge technologies offer transformative benefits far
beyond what was possible mere years ago. The key to leveraging these
benefits effectively lies in successful integration, informed by expert
guidance that treats the laboratory as a unified whole.
3EMERGING TECHNOLOGIES: IMPLEMENTING AI, ML, AND DL TO DRIVE DRUG DISCOVERY
Three Emerging Technologies and Their Drug Discovery Research Applications
Research laboratories around the world are focusing on three key
technologies to overcome production bottlenecks and improve research
outcomes while reducing costs. These technologies represent some
of the most important advances that researchers can leverage when
performing drug development tasks.
• Artificial Intelligence (AI)
Artificial intelligence is a broad discipline that encompasses
automated decision-making and pattern-recognition. It includes
several subset disciplines, including machine learning, computer
vision, and natural language processing.
• Machine Learning (ML)
Machine learning is a subfield of AI that focuses on algorithms that
can learn without human intervention. When these self-learning
systems identify patterns in data sets, they qualify their results to
become better at identifying those types of patterns. There are
three main types of ML algorithms: supervised, unsupervised, and
reinforcement learning.
• Deep Learning (DL)
Deep learning is a subset of machine learning that focuses on
very large data sets. These systems learn from example and filter
out statistical noise to glean insight in ways humans can’t easily
replicate. Convolutional Neural Networks, Recurrent Neural
Networks, and Recursive Neural Networks are three examples of
DL algorithm architectures.
4EMERGING TECHNOLOGIES: IMPLEMENTING AI, ML, AND DL TO DRIVE DRUG DISCOVERY
Each of these disciplines presents a unique set of challenges to bio-IT
research professionals and drug development scientists. Researchers
who are invested in data infrastructure will need to overcome these
challenges with expert guidance.
Artificial Intelligence: Breaking Down Barriers to Clinical Research
Artificial Intelligence presents some of the most exciting opportunities for
bio-IT investment and drug research acceleration. Researchers around
the world are using AI-powered scientific computing engines to support
clinical decision-making processes and optimize drug research.
Since AI is such a wide discipline, bio-pharmaceutical professionals are
using it in many different ways. The sheer number of AI-powered options
and use cases is one of the greatest challenges that bio-IT teams have
to face.
RESEARCHERS AROUND
THE WORLD ARE USING
AI-POWERED SCIENTIFIC
COMPUTING ENGINES
TO SUPPORT CLINICAL
DECISION-MAKING
PROCESSES AND OPTIMIZE
DRUG RESEARCH.
5EMERGING TECHNOLOGIES: IMPLEMENTING AI, ML, AND DL TO DRIVE DRUG DISCOVERY
POWERED BY ELECTRONIC
HEALTH RECORDS AND
WEARABLE MEDICAL
DEVICE DATA, AI ALSO
HAS THE POWER TO
AUTOMATE THE PROCESS
OF CONNECTING
ELIGIBLE CLINICAL TRIAL
VOLUNTEERS WITH THE
SCIENTISTS RUNNING
THEM.
Bio-IT executives know that AI can simplify complex systems and cut
down research costs. The problem is that they only have finite time and
resources, and cannot afford to implement AI-powered processes across
the board—nor should they.
Instead, bio-pharmaceutical executives need to identify the few areas
where AI-powered transformation can have the greatest impact on
research outcomes. Laboratories that focus their integration efforts on
their highest-value processes will earn the greatest long-term benefit
while incurring the least infrastructural risk.
Many bio-pharmaceutical companies have already identified these
AI-ready processes. One of the areas where AI consistently delivers
improvement is in clinical trial recruitment. Until now, if researchers
wanted a standardized, indexable database of eligible trial patients, they
had to make one themselves —and learn a database query language like
SQL to use it.
AI has the power to transform drug manufacturing processes on multiple
levels. The ability to glean insights from AI-powered simulations and
modeling can help researchers focus on the most promising compounds
and pharmacodynamic methods.
Powered by electronic health records and wearable medical device
data, AI also has the power to automate the process of connecting
eligible clinical trial volunteers with the scientists running them. This will
save billions of dollars, improve trial success rates, and even address
problematic selection biases, like the fact that 79% of genomic data
comes from a demographic that only represents 16% of the
world’s population.
6EMERGING TECHNOLOGIES: IMPLEMENTING AI, ML, AND DL TO DRIVE DRUG DISCOVERY
Machine Learning Offers Fast, Accurate Predictions
Machine learning holds great promise in the world of automated
decision-making, personalization of drug therapies, and clinical data
governance. The ability for machine learning algorithms to improve the
accuracy of their insights over time makes them ideal for establishing
analytical roadmaps for the drug discovery process.
Unsupervised ML techniques can predict the therapeutic efficacy of
known and unknown pharmaceuticals. These systems can also play
an important role in predicting the outcome of drug repurposing trials,
and help researchers interpret the molecular mechanisms of
different compounds.
One of the ways that ML can achieve this is by grouping compounds
based on gene expression similarities and clustering the compounds that
have mechanisms of action and biological pathways in common. This is
the essential promise of MANTRA 2.0, developed by the di Bernardo Lab
of the TeleThon Institute of Genetics and Medicine.
7EMERGING TECHNOLOGIES: IMPLEMENTING AI, ML, AND DL TO DRIVE DRUG DISCOVERY
ML-powered technologies can also reduce the cost of toxicity prediction.
Advanced self-learning algorithms can identify similarities among
compounds and predict their toxicity based on input features. eToxPred
uses machine learning to estimate the synthesis feasibility and toxicity of
small organic molecules with promisingly high accuracy.
As with artificial intelligence, research executives and Bio-IT teams need
to work together to identify the best areas to incorporate machine
learning into the drug discovery framework. Particular attention must be
paid to the self-learning nature of machine learning, which relies heavily
on the availability of accurate, well-structured data to derive insight.
This means that some processes are better-suited to machine learning
simply by virtue of having more comprehensive data sets available. High-
quality drug discovery data suitable for machine learning use is relatively
limited in quantity, and must pass stringent validation tests before
entering the drug discovery workflow.
Deep Learning Streamlines Drug Discovery Insights
Deep learning relies on artificial neural networks to simulate the way the
human brain processes information. This typically requires the large-scale
deployment of interconnected computing elements that function in a
way analogous to biological neurons. By mimicking the transmission of
electrical impulses in the brain, neural networks can identify patterns and
solve problems that other technologies cannot.
Since deep learning algorithms typically rely on huge data sets and vast
computational spaces, they are ideally positioned to address some of
the most fundamental problems that bio-pharmaceutical researchers
often face.
8EMERGING TECHNOLOGIES: IMPLEMENTING AI, ML, AND DL TO DRIVE DRUG DISCOVERY
Deep learning engines can recognize hit to lead compounds over a far
wider query space than a human researcher. They can speed up drug
target validation and even help with drug structure design optimization.
Some of the applications for deep learning in drug discovery
include:
• Predicting the 3D structure of target proteins
• Predicting drug-protein interactions
• Determining biospecific drug molecules
• Designing multi-target drug molecules
• Predicting bioactivity and physicochemical properties for drug screening
Deep learning predictions can help researchers identify the best
direction to focus further research. With DL-powered solutions onhand,
researchers will spend far less time traveling down dead-end bio-
pharmaceutical paths.
WITH DL-POWERED
SOLUTIONS ONHAND,
RESEARCHERS WILL
SPEND FAR LESS TIME
TRAVELING DOWN
DEAD-END BIO-
PHARMACEUTICAL PATHS.
9EMERGING TECHNOLOGIES: IMPLEMENTING AI, ML, AND DL TO DRIVE DRUG DISCOVERY
In order to capitalize fully on the predictive power of deep learning,
research scientists will need to identify accurate, high-impact data sets
to target with DL-powered tools. In many cases, data validation will be
necessary to improve overall structure and consistency before running
DL tools.
It’s also important for researchers to pay attention to the economics
of deep learning. Neural network training and operation is resource-
intensive, and comes at a significant cost. DL-powered analyses of very
large systems—on the order of 1.5 billion parameters—can cost between
$80,000 and $1.6 million to train.
This means research organizations need to carefully select the highest-
impact field for deep learning prediction, and pool resources to gain
optimal access to cloud-enabled deep learning infrastructure.
Integrate Next-Generation Technologies in Your Research Lab with Expert Guidance
RCH Solutions is ready to help you identify the research areas best-served
by emerging AI, ML and DL technologies. Our expert consultants can help
you optimize infrastructural investment and leverage new tools in the
most efficient way. Speak with one of our team members to begin the
process of optimizing drug discovery for your research organization.
RESEARCH
ORGANIZATIONS
NEED TO CAREFULLY
SELECT THE HIGHEST-
IMPACT FIELD FOR DEEP
LEARNING PREDICTION,
AND POOL RESOURCES
TO GAIN OPTIMAL
ACCESS TO CLOUD-
ENABLED DEEP LEARNING
INFRASTRUCTURE.
ABOUT RCH SOLUTIONS RCH Solutions (RCH) is a global provider of computational science expertise, helping Life Sciences and Healthcare companies of all sizes clear the path to discovery. For nearly 30 years, RCH has provided focused experience and unmatched specialization designing and deploying cross-functional IT strategies, supporting R&D infrastructure, and offering workflow best practices that solve enterprise and scientific computing challenges.
rchsolutions.com | [email protected]