It is estimated that the cost of developing a new drug is about $ 2.6 billion, which is noticeable. However, much of this is spent on endless experiments and failures. Although a drug starts from the period of the pupae to the final approval, there are countless failures in the middle, and only some people have reflected on whether drug research and development need to be carried out in a way.
The era of "AI+Drug R & D" is coming
For a long time, drug research and development has had the characteristics of a long time, high investment, high risk, and slow investment reporting. The world's tens of billions of dollars are used in drug research and development every year.
There are many ways to "AI+Drug R & D." The core is to scan a large number of scientific papers published by the NLP algorithm, the scientific papers published by a large number of chemical databases, medical databases, and conventional channels, and identify novel drugs, drug genes, and other connections related to treatment, and then Finding potential drugs. AI can quickly analyze the results of the drug structure, the pathological mechanism of the disease, the efficacy of existing drugs, the sample observation of the microscope, and so on, which significantly improves the efficiency of new drug discovery. From the current point of view, AI mainly acts in drug research and development scenarios: target drug research and development, candidate drug mining, compound screening, predictable ADMET properties, pharmaceutical crystal predictions, auxiliary pathological research, and new drug indications.
In positioning targets, AI can sort out and analyze past theses, tests, patents, and clinical information to give the target list of target diseases; in the design of the drug, AI can help design molecular groups that act on targets to target targets. , And find a reliable synthetic method; after synthetic drugs, AI can help screen the drug with the best effect and the smallest toxicity and predict its metabolic situation to improve the efficiency of clinical trials. After entering the clinic, if it fails, AI can find the cause of failure from massive data and reduce losses; in addition to these, AI can also help the drug research team to design clinical trials, recruit and screen the qualified test personnel, analyze the test data generation report ...
The development of drugs has gathered the wisdom of countless people, and the birth of these technologies is no exception. The role of AI is to reduce the time and money cost of research and development as much as possible and strive for the listing of new drugs to bring hope to more patients.
AI technology has attracted attention from the pharmaceutical market.
In recent years, the integration of AI and medical and health has been deepening. Drug development is one of the important areas of AI technology applications. More and more biotechnology companies and artificial intelligence companies have a strong union. Although AI has some uncertainty for drug development, it does not prevent global pharmaceutical companies from layout AI technology. In addition to cooperating with IBM to assist immune tumor drugs, Pfizer also signed a strategic cooperation agreement with Jingtai Technology; Gilard Science Company and Insitro reached an R & D agreement to use machine learning to explore the treatment of non-alcoholic fatty hepatitis (NASH) Innovative targets; AstraZeneca Company and Benevolentai announced that they had reached a long -term R & D cooperation agreement, using artificial intelligence (AI) and machine learning technology, R & D and treatment of chronic nephropathy (CKD) and special pulmonary fibrosis (IPF) Innovation therapy. ATOMWISE, a medical technology company, announced a multi-year cooperation agreement with Lilly.
Giants such as Pfizer, Roche, and GSK have "bet" AI companies, and how to develop for the development is to be tested. After all, there are no successful cases of AI drugs, and no AI-developed drugs have been approved to be listed. Therefore, there is enthusiasm alone, and it only solves a few bottlenecks on the research and development road. Although AI is a unique approach, it still needs time to prove itself.
In the AI era, insightful future trends
1. Pharmaceutical giants+innovative enterprises together to seize the opportunity
The appearance of AI has seen many pharmaceutical companies see hope. In March 2019, Celgene reached heavy cooperation with ExSCientia, which will jointly explore the possibility of using AI to discover and develop tumors and immune drugs; in June 2019, Sanofi and Google (Sanofi) and Google A new virtual innovation laboratory will be established to accelerate the development of new medicines such as artificial intelligence and cloud computing, understanding the treatment process, and improving the efficiency of treatment.
The cooperation is far more than that, and such cooperation is just the beginning. In the future, more strategic cooperation will occur. Innovative companies will also become one of the targets for pharmaceutical giants to acquire in the next five to ten years.
2. Data is king
Big data has become the top priority of this change. With diversification, interdisciplinary, high-quality big data, and accurate processing in the later period, the reform of AI technology can be fully realized. Some enterprises have already been ahead of big data. In April 2019, Medidata established an ACorn AI subsidiary to provide available insights on clinical R&D decisions by allowing data to flow through the entire life cycle from research and development to commercialization. MEDIDATA integrated platform: contains more than 17,000 clinical trial data, of which more than 5,000 active data can analyze 45 billion patient records from 2 million suppliers, powerful clinical databases: the industry's largest structured and standardized clinical database in the industry With more than 4.8 million patients, more than 1,200 customers, and 150,000 certified users.
3. Industry standards need to be established
Another major trend is the establishment of industry standards. Artificial intelligence is subverting the rules of the game. It also needs to be based on the rules in pharmaceutical research and development to achieve consensus and common progress in the industry.
Conclusion
There is no magic for artificial intelligence, and it is impossible to shorten the new drug discovery process from several years or even decades to a few days. But what can be seen is that AI technology is bringing significant changes to the pharmaceutical industry. With the deepening of biomedical understanding and the continuous strengthening of computing power, AI is expected to bring gratifying progress to the medical industry.
Automated blood collection technology was used more than a decade ago. At present, Medicilon has a variety of high-connotation screening new technologies and methods. We has established an advanced drug target screening technology platform. We can collect in a short time in a short time in a short period. Drug target information in all aspects solves the bottleneck of "time-consuming, low accuracy, poor repetitive" of drug target development. Medicilon's Medicinal Chemistry provides customers with new drug research and development services covering various targets and diseases, including discovery from active compounds, drug target screening verification, and the selection of preclinical candidate drugs.
Medicilon can undertake the synthesis of special reagents, intermediates and molecular fragments, preparation of standard products, synthesis design and preparation of impurities or metabolites, synthesis of stable isotope internal standards and synthesis of tritiated compounds.
Related Articles:
Nine Development Trends of Chemical Synthetic Drugs
Six Directions of Chemical Synthesis Drug Research and Development
Contact us:
Email: marketing@medicilon.com
评论
发表评论