Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures. Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains. Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories: 1. Discovery, 2. Toxicity and Safety, and 3. Post-Market Monitoring. We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them. We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.

1. WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

2. Vishnu Vettrivel, Wisecube AI
Drug Discovery and
Development using AI
#UnifiedDataAnalytics #SparkAISummit

3. About me
• Vishnu Vettrivel - vishnu@wisecube.ai
• Data Science/AI platform Architect
• NOT a Molecular Biologist or a Medicinal
Chemist !
• Will be talking about things learnt mostly on
the job
• Have been working with a Molecular biologist
in a Biotech research firm to help accelerate
drug discovery using Machine learning

4. Agenda
• History
– Nature as Source
– Recent efforts
• Rational drug
discovery
– Drug targeting
– Screening
– Drug Discovery
Cycle
• Economics
¡ Computer-aided Drug
Design
¡ Molecular Representation
¡ Drug safety assessment
¡ Demo
¡ Tools and DBs
¡ Resources
¡ Summary

5. History of
drug
discovery

6. Ancient methods: Nature as a source
• Search for Drugs not new:
– Traditional Chinese medicine
and Ayurveda both several
thousand years old
• Many compounds now being
studied
– Aspirin’s chemical forefather
known to Hippocrates
– Even inoculation at least
2000 years old
– But also resulted in many
ineffective drugs
source: https://amhistory.si.edu/polio/virusvaccine/history.htm

7. More recent efforts
• In 1796, Jenner finds first
vaccine: cowpox prevents
smallpox
• 1 century later, Pasteur makes
vaccines against anthrax and
rabies
• Sulfonamides developed for
antibacterial purposes in 1930s
• Penicillin: the “miracle drug”
• 2nd half of 20th century: use of
modern chemical techniques to
create explosion of medicines

8. Rational drug discovery
PROCESS OF FINDING NEW
MEDICATIONS BASED ON THE
KNOWLEDGE OF A BIOLOGICAL
TARGET.
MOST COMMONLY AN ORGANIC
SMALL MOLECULE THAT
ACTIVATES OR INHIBITS THE
FUNCTION OF A PROTEIN
INVOLVES THE DESIGN OF
MOLECULES THAT ARE
COMPLEMENTARY IN SHAPE AND
CHARGE TO THE BIOMOLECULAR
TARGET

9. Drug target identification
• Different approaches to
look for drug targets
– Phenotypic screening
– gene association studies
– chemo proteomics
– Transgenetic organisms
– Imaging
– Biomarkers
Source: https://www.roche.com/research_and_development/drawn_to_science/target_identification.htm

10. Target to drug
cycle
source:
https://www.researchgate.net/publication/294679594_DRUG_DISCOVERY_HIT_TO_LEAD

11. Screening
• High Throughput Screening
– Implemented in 1990s, still going strong
– Allows scientists to test 1000’s of potential
targets
– Library size is around 1 million compounds
– Single screen program cost ~$75,000
– Estimated that only 4 small molecules with
roots in combinatorial chemistry made it to
clinical development by 2001
– Can make library even bigger if you spend
more, but can’t get comprehensive coverage
• Similarity paradox
– Slight change can mean difference between
active and inactive

12. Hit to lead
optimization
source: http://www.sbw.fi/lead-optimization/

13. Drug discovery cycle
Involves the identification of
screening hits using medicinal
chemistry and optimization of
those hits to increase:
– Affinity
– Selectivity (to
reduce the potential of
side effects),
– Efficacy/potency
– Druglikeness
Photo by Boghog / CC BY-SA 4.0

14. economics
source: https://www.nature.com/articles/nrd3681
Eroom’s Law: Opposite of
Moore’s Law – Signals
worrying trends in number
and cost of Drugs to
Market for the Pharma
industry

15. Drug
discovery
timeline
source: https://www.innoplexus.com/blog/five-reasons-to-embrace-data-driven-drug-development/

16. Computer-
Aided drug
design
source:
http://poster123.info/?u=Pharmacological+Strategies+To+Contend+Against+Myocardial

17. Molecular
represent
ation

18. 1-D Descriptors
• Molecular properties often used for
rough classifications
– molecular weight, solubility, charge,
number of rotatable bonds, atom
types, topological polar surface area
etc.
• Molecular properties like partition
coefficient, or logP, which measures
the ratio of solubilities in two different
substances.
• The Lipinski rule of 5 is a simple rule of
thumb that is often used to pre-filter
drug candidates
Source: chemical Reactivity, Drug-Likeness and Structure Activity/Property Relationship Studies of 2,1,3-Benzoxadiazole Derivatives as Anti-Cancer Activity

19. 2-D Descriptors
• A common way of mapping variably
structures molecules into a fixed-size
descriptor vector is “fingerprinting”
• circular fingerprints are in more widespread
use today.
• A typical size of the bit vector is 1024
• The similarity between two molecules can be
estimated using the Tanimoto coefficient
• One standard implementation are extended
circular fingerprints (termed ECFPx,with a
number x designating the maximum
diameter; e,g, ECFP4 for a radius of 2
bonds)

20. qsar
• Predictive statistical models correlating one or
more piece of response data about chemicals
• Statistical tools, including regression and
classification-based strategies, are used to
analyze the response and chemical data and
their relationship
• Have been part of scientific study for many
years. As early as 1863, Cros found that the
toxicity of alcohols increased with decreasing
aqueous solubility
• Machine learning tools are also very effective in
developing predictive models, particularly when
handling high-dimensional and complex chemical
data showing a nonlinear relationship with the
responses of the chemicals

21. SMILE string
• SMILES (“Simplified molecular-input line-entry system”)
• Represents molecules in the form of ASCII character
strings
• Several equivalent ways to write the same compound
– Workaround is to use the canonical version of SMILE
• SMILES are reasonably human-readable

22. Neural fingerprints
• Hash function can be replaced by a
neural network
– Final fingerprint vector is the sum over
a number of atom-wise softmax
operations
– Similar to the pooling operation in
standard neural networks
– Can be more smooth than predefined
circular fingerprints
• Auto-encoders are also used to find
compact latent representations
– converts discrete representations of
molecules to and from a
multidimensional continuous
representation

23. Drug safety assessment
• According to Tufts Center for the Study of Drug
Development (CSDD) the three main causes of failures
in Phase III trials:
– Efficacy (or rather lack thereof) — i.e., failure to
meet the primary efficacy endpoint
– Safety (or lack thereof) — i.e., unexpected
adverse or serious adverse events
– Commercial / financial — i.e., failure to
demonstrate value compared to existing therapy
• According to another study by Yale School of Medicine
– 71 of the 222 drugs approved in the first decade
of the millennium were withdrawn
– Took a median of 4.2 years after the drugs were
approved for these safety concerns to come to
light
– Drugs ushered through the FDA's accelerated
approval process were among those that had
higher rates of safety interventions

24. Tox21 challenge
• Challenge was designed to help scientists
understand the potential of the chemicals
and compounds being tested
• The goal was to "crowdsource" data analysis
by independent researchers to reveal how
well they can predict compounds'
interference in biochemical pathways using
only chemical structure data.
• The computational models produced from
the challenge would become decision-
making tools for government agencies
• NCATS provided assay activity data and
chemical structures on the Tox21 collection
of ~10,000 compounds (Tox21 10K).

25. Deeptox
• Normalizes the chemical representations of the
compounds
• Computes a large number of chemical descriptors that
are used as input to machine learning methods
• Trains models, evaluates them, and combines the best
of them to ensembles
• Predicts the toxicity of new compounds
• Had the highest performance of all computational
methods
• Outperformed naive Bayes, SVM, and random forests

26. Multi-task
learning
• They were able to apply multi-
task learning in the Tox21
challenge because most of the
compounds were labeled for
several tasks
• Multi-task learning has been
shown to enhance the
performance of DNNs when
predicting biological activities
at the protein level
• Since the twelve different
tasks of the Tox21 challenge
data were highly correlated,
they implemented multi-task
learning in the DeepTox
pipeline.
•

27. Associations to toxicophores
• The histogram (A) shows the
fraction of neurons in a layer
that yield significant
correlations to a toxicophore.
With an increasing level of the
layer, the number of neurons
with significant correlation
decreases.
• The histogram shows the
number of neurons in a layer
that exceed a correlation
threshold of 0.6 to their best
correlated toxicophore.
Contrary to (A) the number of
neurons increases with the
network layer. Note that each
layer consisted of the same
number of neurons.

28. Feature
Construction by
Deep Learning.
• Neurons that have learned to
detect the presence of
toxicophores.
• Each row shows a particular
hidden unit in a learned network
that correlates highly with a
particular known toxicophore
feature.
• The row shows the three
chemical compounds that had the
highest activation for that neuron.
• Indicated in red is the toxicophore
structure from the literature that
the neuron correlates with. The
first row and the second row are
from the first hidden layer, the
third row is from a higher-level
layer.

29. Demo

30. Tools and
databases
• Rdkit collection of cheminformatics and machine-
learning software written in C++ and Python.
• DeepChem is an integrated python library for
chemistry and drug discovery; it comes with a
collection of implementations for many deep learning
based algorithms.
• Chembl is a public database containing millions of
bioactive molecules and assay results. The data has
been manually transcribed and curated from
publications. Chembl is an invaluable source, but has
its share of errors — e.g., sometimes affinities are off
by exactly 3 or 6 orders of magnitude due to wrongly
transcribed units (micromols instead of nanomols).
• PDBbind is another frequently used database, which
contains protein-ligand co-crystal structures together
with binding affinity values. Again, while certainly very
valuable, PDBbind has some well-known data
problems.
• https://www.click2drug.org/ website containing a
comprehensive list of computer-aided drug design
(CADD) software, databases and web services.

31. Resources
• Lima, Angélica Nakagawa, Eric Allison Philot, Gustavo Henrique Goulart
Trossini, Luis Paulo Barbour Scott, Vinícius Gonçalves Maltarollo, and
Kathia Maria Honorio. "Use of Machine Learning Approaches for Novel
Drug Discovery." Expert Opinion on Drug Discovery. 2016. Accessed April
23, 2019. https://www.ncbi.nlm.nih.gov/pubmed/26814169.
• Khamis, Mohamed A., Walid Gomaa, and Walaa F. Ahmed. "Machine
Learning in Computational Docking." Artificial Intelligence in Medicine.
March 2015. Accessed April 23, 2019.
https://www.ncbi.nlm.nih.gov/pubmed/25724101.
• Lima, Angélica Nakagawa, Eric Allison Philot, Gustavo Henrique Goulart
Trossini, Luis Paulo Barbour Scott, Vinícius Gonçalves Maltarollo, and
Kathia Maria Honorio. "Use of Machine Learning Approaches for Novel
Drug Discovery." Expert Opinion on Drug Discovery. 2016. Accessed April
23, 2019. https://www.ncbi.nlm.nih.gov/pubmed/26814169.
• Mayr, Andreas, Klambauer, Günter, Thomas, Hochreiter, and Sepp.
"DeepTox: Toxicity Prediction Using Deep Learning." Frontiers. December
04, 2015. Accessed April 21, 2019.
https://www.frontiersin.org/articles/10.3389/fenvs.2015.00080/full

32. Summary
• Increasing pressure is forcing Pharma
industry to turn to AI based techniques to
reduce time, costs and increase success rates
of new drugs to market
• Drug Safety is one of the top reasons for
failures in FDA approvals of new drugs and
recalls
• AI and Deep learning techniques have show
lot of promise compared to traditional
techniques in drug discovery and safety
• The race for using AI is on and over 100 new
startups are now pursuing this line of inquiry

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