We noted you are experiencing viewing problems
-
Check with your IT department that JWPlatform, JWPlayer and Amazon AWS & CloudFront are not being blocked by your network. The relevant domains are *.jwplatform.com, *.jwpsrv.com, *.jwpcdn.com, jwpltx.com, jwpsrv.a.ssl.fastly.net, *.amazonaws.com and *.cloudfront.net. The relevant ports are 80 and 443.
-
Check the following talk links to see which ones work correctly:
Auto Mode
HTTP Progressive Download Send us your results from the above test links at access@hstalks.com and we will contact you with further advice on troubleshooting your viewing problems. -
No luck yet? More tips for troubleshooting viewing issues
-
Contact HST Support access@hstalks.com
-
Please review our troubleshooting guide for tips and advice on resolving your viewing problems.
-
For additional help, please don't hesitate to contact HST support access@hstalks.com
We hope you have enjoyed this limited-length demo
This is a limited length demo talk; you may
login or
review methods of
obtaining more access.
Printable Handouts
Navigable Slide Index
- Introduction
- Overview
- Meet Cinderella!
- Reasons to be cheerful
- The pharmacophore concept (1)
- The pharmacophore concept (2)
- Pharmacophore elucidation
- Pharmacophores in action
- Hit generation and modification tools
- Hit generation
- Hit generation and modification
- Hit modification
- Quantitative Structure-Activity Relationships (QSAR) and Predictive Modelling (PM)
- QSAR & PM: Overview
- QSAR & PM: Data types, descriptors, statistical methods
- QSAR & PM: Model development and validation
- QSAR & PM: Applications
- Quantum Mechanical Calculations (QMC)
- QMC: Introduction
- QMC: Applications
- QMC: Application in discovery of Dorzolamide/TrusOpt®
- Conclusion
- A fairytale ending
- Acknowledgements
Topics Covered
- Pharmacophores and the pharmacophore concept
- Hit generation and modification tools
- QSAR (Quantitative Structure-Activity Relationships)
- Predictive modelling
- Quantum mechanical calculations (QMC)
Talk Citation
Clark, D. (2025, June 30). Ligand-based drug design in the AlphaFold age [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved August 3, 2025, from https://doi.org/10.69645/BIUU5747.Export Citation (RIS)
Publication History
- Published on June 30, 2025
Financial Disclosures
- Dr. David E. Clark is an employee of Charles River Laboratories.
A selection of talks on Methods
Transcript
Please wait while the transcript is being prepared...
0:00
Hello, my name is David Clark.
I work on computer-aided
drug design for
Charles River Laboratories.
I'm going to talk now about
the topic of ligand-based
drug design in
the AlphaFold age.
0:16
The talk breaks down
into three sections:
A very short introduction that
I've called Meet Cinderella,
the main part of the talk is
the section called
Reasons to be Cheerful,
and then there is
just a single-slide
conclusion at the end.
0:34
Let's set the background.
This is what I've
called Meet Cinderella.
I think in some ways,
you've really got
to feel sorry for
ligand-based drug
design at the moment.
There are so many aspects of
structure-based drug design that
have come to the fore over
the recent years that
it's very easy to
forget and overlook
ligand-based drug design.
That's why I've thought of
ligand-based drug design as
a bit of a Cinderella figure.
You can see a picture of
someone like Cinderella in
the middle of the slide there,
feeling rather
sorry for herself,
and that's because of
all these things around
the outside of the slide.
For instance, the
protein databank of
publicly available X-ray crystal
structures of proteins has
grown year on year
remarkably until it passed
over 200,000 structures
back in January 2023.
As if that wasn't enough,
you cannot fail to have heard of
the AlphaFold program:
AlphaFold2, and most
recently AlphaFold3.
With AlphaFold2,
Google's DeepMind was
able to predict more than
200 million three-dimensional
structures for proteins,
giving a huge boost to
structure-based drug design.
Then added to that,
a new experimental
technique called
cryo-EM has come to the fore,
and that's working
really nicely in
complement to X-ray
crystallography to
provide atomic
resolution models of
previously very difficult
study proteins.
Finally, from a
computational aspect,
the application of free
energy perturbation has,
at least in some instances,
shown potential for
accurately predicting
the binding affinity of
compounds in structure-based
drug design projects.
All these things
combine really to boost
the profile and the power of
structure-based drug design,
and as a result,
it's very easy to forget
about or to overlook
ligand-based drug
design methods.
But as I hope to show today,
and as perhaps the clever
rat in the picture says,
well, maybe it's
not all over for
ligand-based drug design.
Let's look at
the reasons why we
should not forget
ligand-based drug design and
continue to use it
in our projects.