By Prashant Sharma
Traditionally,
it requires a lot of time and deep scientific expertise to discover and
synthesize a small molecule that becomes a preclinical candidate. Only 12% of
drug candidates entering clinical trials are approved for use in therapy. With
an average time of ten years for new drug discovery and an investment of
approximately USD 2.6 billion per new molecule brought to the market, the
industry is exploring new avenues to cut down time and costs. A blockbuster
drug takes approximately 12 years and $4 billion-$11 billion of investment (1).
The
emergence of machine learning (ML) and artificial intelligence (AI) can offer
researchers guidance on processing, analyzing, and understanding the data and
its extensive application. Utilizing AI-ML in drug design represents an
advanced approach that can help reduce the timeframe for identifying targets
and developing new drugs. The researchers aim to integrate ML with
small-molecule drug discovery and continue making groundbreaking strides in the
continuum of personalized healthcare.
The traditional
small molecule discovery process uses manual testing and assays before adopting
high-throughput screening. Computational methods and virtual screening were
introduced to speed up the process, followed by today's increasingly
sophisticated AI and ML techniques.
The application of
AI in small molecule drug discovery helps us increase speed, lower costs,
improve success rates, and boost innovation. Furthermore, AI model algorithms
can help tackle large-scale datasets, empower researchers to predict molecular
interactions, refine drug candidates, and enhance the overall drug development
process. AI-ML helps medicinal chemists speed up their tasks and notably
shorten the below phases:
·
Protein structure prediction
and understanding of structure-activity relationships.
·
ADME and toxicity predictions.
·
Accelerate synthesis planning
for novel compounds.
·
Improve compound screening.
Pharmaceutical
companies must upskill the chemists and adopt a clear AI adoption strategy. The
steps to do so are:
·
Study the impact of AI on
medicinal and synthetic chemistry.
·
Build the training plan
internally for the chemists to acquire AI skills.
·
Collaboration and forming
internal teams to speed up the learning process.
·
Follow the Investment with the greatest
impact on research.
Designing new
molecules likely to interact with a target, synthesizing, and then testing
those molecules to identify the most promising candidates is time and
resource-intensive. AI has significant potential to accelerate the
design-make-test-analyze (DMTA) cycle and reduce the number of iterations (2).
At each stage of DMTA, AI can be used to:
·
Design: Protein structure prediction, de-novo library design, virtual
screening, synthetic accessibility, and molecular property prediction.
·
Make: Plan synthesis of new molecules, predict their yield and purity,
and identify problems with the synthesis process.
·
Test: Screen new molecules for the ability to interact with target
proteins, predict efficacy and toxicity, and identify the most promising drug
candidates.
·
Analyze: Process large volumes of test data to identify correlations and
trends and design further experiments to test the most promising drug
candidates.
Furthermore, AI
helps significantly in lead identification and optimization by increasing the
time and efficiency of the most expensive and time-consuming phases of
preclinical drug discovery:
·
Hit identification: 30 to 50 percent acceleration in small molecule high-throughput
screening, using approaches such as molecular property prediction in an
iterative screening loop (versus the existing approach of randomized selection
of compounds). (2)
·
Lead optimization: more than double improvement over baseline on the key metric of
"efficacy observed," over 100 times the number of in silico
experiments possible compared with previous screening, and faster design of
compounds for optimization of drug delivery efficacy in lead optimization. (2)
Figure 1: How AstraZeneca applies AI to accelerate the DMTA cycle –
including synthesis planning, condition prediction, and molecular ideation.
View the webinar on
AI used by AstraZeneca for Reaction Prediction: https://webinars.elsevier.com/elsevier/Webinar-2-Drug-Discovery-with-AI-at-AstraZeneca-from-Generative-Models-to-Reaction-Prediction.
Over the last 10
years, key AI models have emerged for faster small-molecule discovery. These
models predict the three-dimensional protein structure to understand the active
site and optimize the compound design to modulate desired interactions. Gen AI
models are used to create virtual compound libraries and screen novel chemical
compounds to create a virtual compound library. Using DL, models forecast a
molecule's properties based on its structure, which is crucial for drug
discovery as structure determines its interactions within a person.
The statistical
QSAR model is based on training data that pairs chemical structures and
biological activities. QSAR is used to predict the biological activity of a
chemical compound from its structure, including toxicity, drug efficacy, and
ADME properties. There are two types of QSAR models: linear QSAR models assume
a molecule's biological activity is always linearly related to its chemical
structure. In contrast, nonlinear QSAR models allow the nonlinear relationship
between biological activity and chemical structure.
Figure 2: AI Models
in Small Molecule Discovery
Quantitative
structure-property relationship (QSPR) uses machine learning to relate
molecular structures to compound properties and speed up the DMTA cycle. ML
algorithms find structural or chemical patterns that correlate with specific
compound properties, such as activity against the target of interest,
reactivity, solubility, and adsorption. Synthetic accessibility models based on
ML and DL score the ease of synthesis of compounds, allowing chemists to narrow
down libraries to sets of synthetically accessible compounds. Computer-aided
synthesis prediction (CASP) saves time, improves accuracy, and helps medicinal
and synthetic chemists control costs by reducing synthesis failures and
validating proof of concept at the earliest stage.
Training AI
algorithms for drug discovery requires a significant amount of data. Large
datasets containing chemical structures and information about their biological
activity are crucial for building effective models.
Public databases
are one resource for this data, but commercially available options like GOSTAR®
by Excelra offer additional features. These features can include a wider range
of chemical structures, curated data sets focused on specific diseases, and
tools specifically designed for drug discovery.
Despite the
traditional slowness and expensive nature of small-molecule drug discovery, AI
is revolutionizing the field by analyzing large data sets for patterns. Faster
development timelines, reduced costs, improved by better drug candidates, and
success rates. By embracing AI models and upskilling experts, pharmaceutical
companies can open a new era of innovation in drug discovery.
References
(1) https://www.excelra.com/blogs/data-in-healthcare-how-far-we-have-come/
(2) https://www.elsevier.com/en-in/industry/ai-in-small-molecule-drug-discovery
(3) https://www.excelra.com/databases/gostar/ai-ml/
Pharmaceutical Microbiology Resources (http://www.pharmamicroresources.com/)
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