How Exscientia Reduces Drug Discovery Time With Gen AICompany Addresses Speed, Cost, Efficacy and Safety Issues With Application of Gen AI
While generative AI tools, such as ChatGPT, have gained immense popularity, they represent the initial breakthrough for healthcare organizations. However, the true transformation in drug discovery is being driven by advanced large language models that surpass ChatGPT's capabilities.
U.K.-based Exscientia, a pioneer in precision medicine, is harnessing the power of generative AI to accelerate the identification and development of candidate molecules. This approach extends to both its internal pipeline, featuring wholly or partially owned product candidates, and collaborations with other leading biopharmaceutical companies. Exscientia's expertise lies in the realms of drug discovery, design and development, marking a significant stride toward the future of pharmaceutical innovation.
Drug discovery process has multiple challenges that include higher cost, slow pace and high degree of uncertainty in the success of clinical trials. According to a study, the average cost of taking a drug from discovery to market is about $1.8 billion, one-third of which was spent on drug discovery itself. The cumbersome process takes anywhere between three-six years.
Exscientia is addressing these challenges using generative AI. The AI-designed molecules not only make the drug discovery process faster, but also increase the probability of clinical success. The company has already discovered a few molecules using its generative AI platform, of which six have already progressed into the clinical setting.
The sixth, and most recent generative AI-designed molecule to enter Phase 1 trials in May this year, is reported to be a highly selective bispecific small molecule with broad potential in psychiatric disease. It was created under the company’s collaboration with Sumitomo Pharma.
In 2021, Exscientia, in collaboration with biotech firm Evotec, unveiled the commencement of Phase 1 clinical trials for a ground breaking anticancer compound. This innovative drug candidate came into being with Exscientia's 'Centaur Chemist' AI design platform, a process that condensed what would have traditionally been a protracted four-five-year timeline into eight months. Exscientia's AI platform is not just limited to oncology; it is being harnessed to explore treatments for a diverse spectrum of diseases, including cancer, Alzheimer's disease and pain management.
In a recently released press statement, Exscientia's founder and CEO, Andrew Hopkins said the Centaur Chemist platform allows the company to move rapidly from idea generation to new drug molecules ready for Investigational New Drug (IND) and clinical development.
Below are three use cases of Exscientia’s AI platform in the drug discovery process:
- Screen large libraries of potential drug candidates to identify those that are most likely to bind to a target protein, narrowing down the search for promising drug candidates;
- Design new drug molecules tailored to interact with a target protein, improving the potency and selectivity of drug candidates.
- Predict the properties of drug candidates to assess their potential safety and efficacy.
"Using AI, we are creating a much more efficient process to design and develop differentiated drug candidates. We believe that all new therapies will be designed with the help of AI in the future," Hopkins said in a company’s press release.
To support its generative AI capabilities, Exscientia recently announced the opening of its automation laboratory in Oxfordshire to integrate generative AI and automation for faster and high-quality experimentation.
With the potential to revolutionize the way new drugs are designed and discovered, Gartner has predicted over 30% of new drugs and materials to be systematically discovered using generative AI by 2025.
Startups With Generative AI Solutions for Drug Discovery
Insitro: Analyzing vast biological datasets to generate novel therapeutic candidates to enable drug discovery, expedite drug development and optimize candidate selection.
Atomwise: Developing new drugs for rare diseases by designing new molecules that are more likely to be effective in treating rare diseases.
Exscientia: Designing new molecules for developing new drugs for cancer that are more likely to be effective in treatment.
Deep Genomics: Analyzing the vast genetic data to identify disease-causing mutations and predict the impact of specific genetic variations, helping accelerate the discovery of novel therapies.
Mendel.ai: Analyzing clinical data and matching eligible patients with appropriate clinical trials to streamline the trial recruitment process, helping accelerate clinical trial matching.