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About Biomedical Basics
Biomedical Basics are AI-generated explanations prepared with access to the complete collection, human-reviewed prior to publication. Short and simple, covering biomedical and life sciences fundamentals.
Topics Covered
- HTS principles and workflow
- Automation in screening
- Assay development and validation
- Data analysis and hit ID
- Managing false results
- Limitations and challenges of HTS
- Technological advances in HTS
Talk Citation
(2026, October 29). High-throughput screening [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved October 29, 2026, from https://doi.org/10.69645/IZTY9278.Export Citation (RIS)
Publication History
- Published on October 29, 2026
Financial Disclosures
A selection of talks on Pharmaceutical Sciences
Transcript
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0:00
This presentation will examine
high throughput screening with
a focus on the principles and
workflow of high
throughput screening,
HTS in drug discovery,
highlighting the automation and
robotics central to large
scale compound screening.
We will discuss assay
development and validation,
data analysis for identifying
potential drug candidates,
and strategies for dealing with
false positives and negatives.
Finally, we address the
limitations of HTS and explore
how technological
advancements are working
to overcome current
challenges in the field.
High throughput screening or
HTS is a key part of
modern drug discovery.
It automates the testing of
thousands to millions
of compounds
against a biological target to
find potential modulators
of its activity.
HTS uses miniaturized assays in
96 384 or 536 well plates,
each containing the target
and a unique compound.
Advances in biotechnology,
robotics and
informatics allow companies to
screen vast chemical
spaces in weeks,
boosting the chances of
finding effective molecules.
The heart of HTS is automation.
Robotics manage precise
liquid transfers,
plate handling, and ensure
reproducibility across
thousands of samples.
Spectrometry and imaging systems
allow for rapid, sensitive
data collection.
Developing a robust
assay is crucial.
It must be sensitive,
reproducible,
cost effective, and
suitable for automation.
Validation includes controls and