With years of experience in building and scaling global companies, CEO Joanna Shields is now focused on building technology in the service of science at BenevolentAI

Drug discovery scientists are all aiming to identify compounds and candidate drugs with ‘good’ properties that are safe and efficacious, as quickly and cheaply as possible. The standard approach of the last 20 years has been to identify a single molecule disease target, and then to identify a compound that interacts with and modulates this target with high specificity. However, there is now a growing realization that this ‘one target – one drug’ approach doesn’t work well, and that screening huge libraries of compounds against one particular property of an isolated target is an inefficient way to discover potential drugs. Much of the innovation currently seen in drug discovery methodologies seeks to access and integrate more information – about targets, compounds, and disease phenotypes – to enable a more comprehensive and holistic approach to discovering ‘good’ drug candidates. This article does not try to crystal ball-gaze deep into the future, but rather to identify those trends in the adoption of new technologies and approaches that are gaining traction now, and that can be expected to become more prevalent in the next two to three years. While the objectives of drug discovery don’t change, the methods and techniques by which pharmaceutical companies, biotechs and academia discover new drugs are evolving at a significant pace – and they need to. BenevolentAI is the global leader in the development and application of artificial intelligence (“AI”) for scientific innovation. The company aims to accelerate the journey from inventive ideas to medicines for patients by developing AI to generate new treatments for some of the world’s 8,000 untreated diseases. 

Team of technologists, AI researchers, and scientists builds and applies AI to the entire drug discovery and development process. The technology unlocks what is not known by creating new ideas based on established facts to provide a different understanding of disease, which leads to inventive steps and real insights that drive the development of new medicines and treatments. Many of the top selling drugs do not work for the patients they are prescribed for. BenevolentAI uses AI and machine learning models to understand the patient's unique underlying mechanisms that cause disease and those insights help engineer new medicines that work better. Developing a drug can take a decade or more and the overwhelming majority of new drug programmes fail. By re-engineering drug discovery and identifying why patients are more likely to respond to treatment. It costs billions to bring a new drug to market, leaving many disease areas neglected under the current economic model. By applying advanced technology we can drive down the costs of drug development and bring new hope to patients. Advanced technologies are helping to unlock the potential of biomedical data and extract valuable insights like never before. However, behind these new technologies lies a data gap that threatens to exacerbate existing health disparities. BenevolentAI has made it their mission to join forces across industries to create tangible solutions to this urgent issue.

Mind the gap: the diversity issue in medical research

The data gap in clinical research is well documented, but now that AI methods are being applied at all stages of the drug discovery and development journey, the existing data gap in clinical research could be exacerbated if action is not taken now to diversify the pool. Disease patterns, clinical presentation and therapeutic response are strongly influenced by factors such as gender, race/ethnicity, ancestral background and socioeconomic status. However, an analysis of drug studies shows that most participants in clinical trials are likely to be male and from western ancestries. Furthermore, we can see from recent advances in omics, for example in genome-wide association studies (GWAS), the data comes from a high percentage of patients with western ancestries. African DNA makes up less than 2% of genetic research material, despite people of African ancestry being more genetically diverse than all of the other populations in the world combined. AI systems are only as good as the data they use. At Benevolent they are committed to raising awareness of the data gap issue and are proactively engaging stakeholders in order to shake up the status quo.

Meet the leader behind the success of BenevolentAI

Joanna Shields, CEO of BenevolentAI is a tech industry veteran with over 25 years of experience in building and scaling global companies including Facebook, Bebo, AOL, Google, Real Networks and Efi. Prior to joining Benevolent AI, she served in UK Government as Under Secretary of State and Minister for Internet Safety & Security, Digital Economy Adviser and Chair and CEO of TechCityUK. Joanna is passionate about creating responsible technology that benefits humanity. She is a leading advocate for children's rights and safety online and founder of the WeProtect Global Alliance, and a Life Peer of the House of Lords.

“Every day there is thousands of scientific papers published, hundreds of millions of medical records created, vast amounts of data collected and research completed. Our machine learning models and algorithms empower scientists to make sense of all of that information.”