The Role of AI drug R&D Startups in Early Drug Development Projects
SummaryCompared with traditional methods, AI can discover new molecular compounds or emerging drug targets faster, thereby speeding up the drug development process.
- Author Name: Lisa George
Digital technology and artificial intelligence (AI) are driving a revolution in the medical and health field. As more and more large pharmaceutical companies encounter bottlenecks in new drug development, they choose to cooperate with artificial intelligence drug R&D startups. As one of the most core links in the pharmaceutical industry, drug research and development is an important area where artificial intelligence technology can show its great talents.
Compared with traditional methods, AI can discover new molecular compounds or emerging drug targets faster, thereby speeding up the drug development process. At the same time, it can more accurately predict the subsequent experimental results of new drugs, thereby increasing the success rate of each stage of the drug development process as much as possible. More specifically, AI works by summarizing the clinical medicinal effects and molecular dynamics characteristics of targeted drugs composed of different protein sequences, molecular structures, and small molecules, as well as analyzing the binding force and stability between the targeted molecules and cancer cell receptors, and then uses these data to train the model to achieve accurate prediction of the model.
From the perspective of large pharmaceutical companies and biotechnology companies, the use of AI technology can enable them to simplify drug development work, including integrating large amounts of patient data into easily manageable and reliable information, and ultimately and significantly reduce drug costs and development time. Therefore, it is not strange to see that large pharmaceutical companies are now working with AI startups to develop new drugs and treatment options.
Since 2012, Merck and GlaxoSmithKline are the two pharmaceutical companies that have the closest cooperation with AI drug development startups, followed by Bayer, Takeda Pharmaceutical, AstraZeneca, Sanofi and Roche. Although some cooperative projects did not disclose specific research drug fields, neurodegenerative diseases and cancer were the two hot research topics based on disclosed information. Some projects also target at finding a cure for cardiovascular and gastrointestinal diseases.
Merck is one of the first pharmaceutical companies to cooperate with AI drug research and development startups. In 2012, Merck and Numerate cooperated to develop treatments for cardiovascular diseases. Numerate, located in San Francisco, USA, mainly uses machine learning software to develop new treatments for neurodegenerative diseases (such as Parkinson’s disease and Alzheimer’s disease), cardiovascular disease and tumors, and is committed to developing drug design platforms for companies specialized in small molecules drug development. Many of Numerate's collaborations with pharmaceutical companies are based on their ADME and toxicity prediction capabilities. In June 2017, Takeda Pharmaceuticals signed a contract with Numerate to use Numerate’s platform to develop small molecule drugs for oncology, gastroenterology and central nervous system diseases. In the same month, Numerate announced a collaboration with Servier to design a small molecule modulator of the ryanodine receptor (RyR2). This target is believed to play an important role in cardiovascular disease, but it does not yet have drug treatment capabilities. This collaboration may result in emerging treatments for heart failure and arrhythmia. In January 2019, Numerate announced its collaboration with another pharmaceutical company to develop clinical drug candidates for the treatment of central nervous system diseases.
MedAI is another AI drug R&D company that has successively launched a number of product prototypes, from the early development stage (AI-driven drug synthesis, drug design, drug activity prediction) to the clinical research stage (AI-driven pharmacovigilance system, registration transaction system, clinical data programming system) and so on, covering a series of key nodes in the whole process of new drug research and development.