Natural compounds sourced from plants hold great promise for treating a variety of human diseases.
Yet, the journey from these complex molecules to viable therapeutic options in the lab has traditionally been a tedious and time-consuming process, often characterized by trial and error.
Advancements in Drug Discovery
A team from Scripps Research has made significant strides in this area.
Their recent study reveals how advanced computational tools can dramatically speed up the synthesis of intricate natural compounds.
They successfully synthesized 25 different picrotoxanes—compounds derived from plants known for their potential to impact brain functions.
Ryan Shenvi, PhD, a leading professor at Scripps Research and the study’s senior author, highlighted the challenges presented by these complex plant compounds in the realm of drug development.
Historically, trying to manipulate them has been fraught with difficulties.
However, the fusion of virtual modeling techniques with experimental validation offers a groundbreaking advancement in the design and synthesis of molecules.
Picrotoxanes and Their Potential
Picrotoxanes, extracted from the seeds of certain shrubs native to Asia and India, interact with the mammalian nervous system.
They bind to the same brain receptors targeted by well-known medications like Valium, which addresses anxiety and sleep issues.
These compounds have also found roles in various cultures as pesticides and fish toxins.
Because of their potential therapeutic uses, Shenvi and his team are keen to explore their effects on brain function further, although synthesis in lab settings has previously been a barrier to deeper investigation.
Shenvi pointed out the complexity of picrotoxanes’ atomic structures, which complicates predictions about how they behave.
The researchers learned that synthesizing one compound does not guarantee that others, which may look similar, can be created in the same way.
Innovative Synthesis Methods
To tackle the synthesis of picrotoxanes, Shenvi and graduate student Chunyu Li turned to advanced computer modeling techniques.
They set out to predict new synthesis routes using simple chemical building blocks.
Their first step involved creating a virtual library of potential intermediate compounds that could arise during synthesis.
Employing Density Functional Theory (DFT), they evaluated these intermediates to pinpoint which would likely work best for producing neuroactive compounds.
When they put their modeling predictions to the test, the results were promising.
They tried five different synthesis pathways for picrotoxanes, and the outcomes matched their predictions: three pathways succeeded while two failed, just as they expected.
Shenvi admitted he was initially doubtful about using DFT for predictive analysis rather than merely for interpreting experimental results.
However, the success of their modeling surprised him.
Despite DFT’s effectiveness, it can be resource-intensive when assessing potential intermediates.
To streamline this process, Shenvi and Li harnessed a pattern-recognition technology similar to those used in modern AI applications.
This method allowed them to uncover trends within the DFT results and create a new statistical model that significantly expedited the prediction of reaction outcomes.
With this innovative approach, they successfully identified synthesis methods for 25 picrotoxanes and confirmed these in the lab.
Li shared enthusiasm for their novel strategy, emphasizing that it not only enabled the creation of picrotoxanes but also laid the groundwork for chemists facing similar synthesis challenges.
The research team is already applying this new methodology to explore further problems and plans to investigate the biological effects of the 25 synthesized picrotoxanes in mammalian systems, utilizing advances in natural language processing to aid in their research.
Source: ScienceDaily