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Hudson Electronic Scientist – HES

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Hudson's Electronic Scientist - HES

Hudson’s Electronic Scientist (HES)

Hudson’s Electronic Scientist (HES) is a powerful new tool designed to assist researchers discover new drug molecules.  HES puts together some of the most powerful computational chemistry tools together in a novel, iterative manner that finds biologically active molecules in a large compound library with incredible efficiency.  In addition, HES is an expert system that learns more about your target every iteration, and uses to this knowledge to build 3-dimensional models of your system to provide medicinal chemists information that will help them convert your newly discovered leads into clinical candidates.

How does it work?

HES works on the simple principle that a molecule binds tightly to a protein target for two main reasons:

  1. Pharmacophore: The molecule contains key chemical features laid out in a particular 3-dimensional relationship.  The more another molecules possesses similar features, the more likely it will exhibit a similar biological profile. 
  2. Shape: The molecule fits the active site of the protein in such a way as to maximize non-covalent chemical interactions and maximized displacement of water from the empty binding site.

The better a molecule matches the ideal requirements of its protein target, the greater it’s binding affinity to that target.  Also, a molecule that binds to the active site with great potency is less likely to bind to other targets, thus reducing its side effect profile.

HES searches a library of molecules and identifies the one that are most likely to act by the same mechanism.  Unlike competing systems, HES will find molecules that contain structurally different scaffolds.  It is therefore an excellent tool for finding novel, patentable molecules, as wells as molecules that possess enhanced selectivity and a better ADME/Tox profile.

HES compares the Pharmacophore and shape features of biological active structures and identifies a small selection of candidate molecules to be tested in an appropriate assay.

A key difference between HES and competing systems is the iterative nature of the procedure.  HES reads the biological assay results obtained for the candidates and carries out a new analysis of the library.  HES becomes more “knowledgeable” about your system after each cycle of analysis/testing

Why is HES Better?

A frequent question asked about HES is “We have similar tools and in-house expertise, why should we consider HES?”

There has been over twenty years of research behind the analyses automated within the HES process.  Although several standard computational chemistry applications are used, it’s the wide variety of proprietary algorithms that we have developed that leads to the outstanding results consistently achieved. 

The key question to be asked on a given project is “What is the ideal Pharmacophore and shape required by the target protein?”

Most virtual screening systems simply make the assumption that the required features are present in the available molecules possessing the desired activity.  Such molecules are typically used as the query in a virtual screen and a list of similar molecules are retrieved from a compound collection for testing.  The results are rarely impressive.

In most cases, the query molecule will be less than perfect.  It may only interact with a portion of the binding site.  Or it may contain additional features that do not interact with the binding site.  In either case, the virtual screen doesn’t know the relative importance of each feature, so the list of hits obtained from such a search will be biased by all of its characteristics.  The longer the list, the weaker the relationship between the hits and the query; therefore, the lower the likelihood they will share the same biological activity.

Why did Hudson Robotics make HES?

Hudson Robotics has been involved in robotic automation for over 30 years, the majority of that period we have been focused on supporting the biomedical, biotechnology and drug discovery community.  Six years ago, we began an intensive project to combine our expertise in the automation of biological assays with the expertise we obtained in computational chemistry, molecular modeling and drug design.  HES is one of the direct results of this initiative.


We have tested the system with probe molecules that are known to bind to 10 different biological targets against a library containing 80,000 diverse structures.  In every case, we have found multiple active molecules that possess different scaffolds in the very first plate of 100 suggestions.  In fact, on average, we found over 35% of the known active molecules in that first plate.  To obtain similar results from a typical high throughput screen, one would have to test an average of 28 such plates.

HES was employed by Scripps Research Institute in Florida to help them breathe new life into their anti-addiction project focused on positive allosteric modulators of GABABR receptor.  Their compound library contains over 610,000 molecules.  After only three (3) iterations, HES uncovered more than 30 active molecules, representing 5 novel chemical scaffolds.  In addition, HES identified 17 therapeutically valuable negative allosteric modulators operating at the same site.  All of this after testing only 300 molecules which represents less than 0.05% of the Scripps compound library.

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