Blog

Using Artificial Intelligence to Optimise Small-Molecule Drug Design

Here at BenevolentAI, we are unlocking the power of scientific data so no disease goes untreated.

We achieve this in a variety of ways, right from initial target identification and validation, through hit discovery and lead optimisation, and finally into the clinic, making us the only end-to-end drug discovery company driven by Artificial Intelligence (AI) in the world.

We power this innovation by using structured and unstructured data sources to learn new insights and relationships at scale that otherwise would not be possible. Here, a key differentiator is how we use AI to extract the knowledge locked in the scientific literature and patents to boost our knowledge graph of entity relationships of genes, targets, molecules, and diseases.

My role at BenevolentAI, as head of the Chemoinformatics team, is to guide the scientific direction and validity in the development of our molecular design platform. The size of drug-like chemistry space is truly vast. As an analogy, if we take six Lego bricks, it is possible to construct them in almost one billion unique configurations. Replacing the bricks for atoms, and scaling the number up from six to the more typical size of a drug-like molecule of 20-30 heavy atoms, the size of the space expands dramatically to truly astronomical proportions. The size of this space makes it technically challenging to exhaustively examine every theoretical molecule, instead we use advanced AI algorithms to effectively sample that space to explore and exploit the most promising candidates to take to synthesis and testing.

Drug discovery itself is an inherently multiobjective optimisation process, with many different parameters needed to be optimised in concert. We score each of the candidate solutions with multiple predictive models using a range of appropriate parameters, including the introduction of synthetic tractability, and even planning synthetic routes.

The platform we have developed at BenevolentAI gives us the power to not only tell our scientists what to make, but also how to make the molecules that are of most relevance to our drug discovery programmes, thereby helping to optimise the whole of our AI-driven drug discovery pipeline.

More Posts

You Might Also Like

News
BenevolentAI’s platform-derived hypothesis for COVID-19 treatment validated in US NIAID randomised control trial
NIAID trial shows baricitinib in combination with remdesivir reduces the recovery time in patients hospitalised with COVID-19, validating BenevolentAI’s AI-derived hypothesis.
Sep 14, 2020
Blog
Building the next generation of leaders with Circl
BenevolentAI Director of Talent Acquisition and Development, Yasmina Rahiman, explores the power of coaching for leadership development in technology.
Aug 21, 2020
News
Clinical data validates BenevolentAI's AI predicted hypothesis for baricitinib as a potential treatment for COVID-19
Research published in EMBO Molecular Medicine confirms AI predictions for anti-viral and anti-cytokine signalling effects of baricitinib in critically hospitalised COVID-19 patients
Jul 1, 2020
News
COVID-19 and AI: An editorial review in EMBO from Michael B. Schultz, Daniel Vera, David A. Sinclair
David Sinclair and colleagues review our recent publication in the EMBO Molecular Medicine Journal in support of our AI-derived hypothesis for a potential treatment of COVID-19.
Jul 1, 2020
Video
How do we get the next 10 years right? w/ Joanna Shields, CogX
“We need leaders with empathy who care about their fellow citizens and are prepared to work to end injustice and create opportunities for all". Our CEO Joanna Shields opens CogX 2020 sharing inspiration for the next decade.
Jun 8, 2020
Blog
Kindness matters: mental health and wellbeing through COVID-19 and beyond
To conclude Mental Health awareness week, we share some thoughts about the power of kindness and how building a culture of openness will help shape the future of business.
May 27, 2020