Avicenna Introduces Machine Learning-Enhanced Medicinal Chemistry Platform to Accelerate the Last Mile of Small Molecule Drug Discovery
May 08 2024 - 2:00PM
Business Wire
Peer-reviewed research finds the
company’s novel technology enables faster dataset construction,
further shortening Avicenna’s timelines to develop life-saving
medicines.
Avicenna Biosciences today introduced an extension to its
machine learning (ML) technology platform to enhance medicinal
chemistry and expedite clinical-stage drug discovery. The company
has raised $14.5 million in funding to date, with DCVC Bio leading
its 2022 seed round, and this month published a paper in the
peer-reviewed Journal of Chemical Information and Modeling.
Co-authored with researchers from Schr�dinger and Microsoft
Research AI4Science, the paper outlines how combining Schr�dinger’s
physics-based methods with Avicenna’s novel ML methods can make the
lead-to-drug optimization phase of small molecule drug discovery
faster, less expensive and more successful – particularly when it
comes to engineering potency and selectivity against a potential
biological target.
“We’re accustomed to hearing scientific success stories, but the
countless failures that happen along the way often get overlooked.
In medicinal chemistry especially, failure is prevalent. It can
require hundreds of millions of dollars across many clinical
attempts to bridge the complex gap from chemistry and biology to
medicine, and successfully develop an approved drug,” said Dr.
Thomas Kaiser, co-founder and Chief Scientific Officer at Avicenna.
“Avicenna is applying novel ML methods to navigate the unknowns in
medicinal chemistry and design around them. Our methods allow a
drug design team to learn from years of failure and optimize
against validated targets more efficiently. We’re making the most
critical phase of drug design — the last mile of drug discovery —
substantially faster and more cost effective.”
Technology Applications and Results
Avicenna is leveraging its technology to develop its own
therapeutic programs, with an initial focus on neurodegenerative
diseases. For example, Rho kinase (ROCK) inhibitors demonstrate
potential in neurodegeneration and metabolic diseases. However,
designing an orally dosed, central nervous system-penetrant ROCK
inhibitor has proved extremely difficult. Fasudil, a promising ROCK
inhibitor currently in Phase 2 clinical trials, must be dosed
intravenously twice daily, precluding its use in illnesses like
chronic kidney disease or neurodegeneration. To overcome these
challenges, Avicenna has initiated its own ROCK Inhibitor Program.
Using its novel ML technology to identify drug-like compounds with
desired pharmacokinetic properties, the company has already
achieved:
- Faster Timelines: 9 months from idea to in vivo proof of
concept
- Reduced Costs: $220,000 from concept to initiation of
Investigational New Drug (IND)-enabling studies
- Better Therapeutics: Two development candidates
discovered while only synthesizing 11 total compounds
Research Paper Implications
“Creating new medicines and safely delivering them to the people
who need them is incredibly difficult and full of risk. Avicenna is
working to make drug discovery straightforward and fast by creating
new algorithms to identify molecules with ideal drug-like
properties,” said Dr. John Hamer, Managing Partner at DCVC Bio.
“Avicenna’s collaborative research with Schr�dinger and Microsoft
demonstrates how a physics-based augmentation of ML requires just
tens of molecules to optimize small molecules against a new drug
target, as opposed to thousands. We couldn’t be more excited to
support this potentially game-changing biotechnology company as it
scales to its next stage of growth.”
Titled “FEP-Augmentation as a Means to Solve Data Paucity
Problems for Machine Learning in Chemical Biology,” Avicenna,
Schr�dinger, and Microsoft’s newly published paper describes how to
use physics-based methods such as Schr�dinger’s free energy
perturbation technology, FEP+, to generate virtual data, which can
be used to augment the sparse data sets commonly found in early
medicinal chemistry optimization. These augmented datasets can be
used for ML training where it was previously impossible to train
without the disclosed approach. The augmentation is comparably
informative for ML training as going through the effort and expense
of making and testing the needed compounds. Ultimately, this allows
an early-stage discovery team to access ML which can then quickly
query millions of lead-like compounds and identify promising leads
for drug development. The paper outlines the key mechanistic
considerations for augmenting such data sets and lays the
groundwork for implementation, demonstrating that an initial series
of 10-20 related compounds, accompanied by 3D structures
co-resolved with a small set of ligands, can serve as an
accelerated foundation for lead optimization.
The research shows that combining FEP+ with ML can accelerate
the hit-to-lead phase of drug discovery, which can generate
profound implications, including:
- Shorten the time spent in hit-to-lead optimization
- Significant reduction in synthetic effort
Team and Partnership Opportunities
The Avicenna team’s collective background in mathematics,
chemistry and medicine brings a uniquely comprehensive perspective
to their core discipline of medicinal chemistry. Co-founders Drs.
Kaiser and Pieter Burger met while working within the Liotta
Research Group at Emory University. The group is well known for its
successes in drug development across virology, oncology and
neurology, creating more than 20 FDA-approved therapeutics. As a
synthetic organic chemist, Dr. Kaiser led the Liotta antivirals
group, and as a structural bioinformaticist, Dr. Burger led its
computational group.
In addition to developing its own therapeutic programs, Avicenna
partners with clinical-stage biotech startups and major
pharmaceutical organizations to optimize their drug discovery
campaigns and maximize risk-return profiles, easily fitting into
partners’ existing chemistry workflows. For more information on
Avicenna and its technology platform or to inquire about
partnership opportunities, visit www.avicenna-bio.com.
About Avicenna Biosciences
Founded in 2020, Avicenna is on a mission to solve the
intractable drug design challenges that previously stopped drug
candidates in their tracks. The company’s machine learning-driven
medicinal chemistry platform makes lead-to-drug optimization
faster, cheaper and more successful – transforming sub-optimal
clinical candidates into life-saving drugs. Avicenna is backed by
DCVC Bio, with Entrepreneur-in-Residence Christopher S. Meldrum
having joined the Company as President and CEO. For more
information, visit www.avicenna-bio.com and follow the company on
LinkedIn.
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Media Contact Kerry Walker kerry@walkercomms.com