The application bottleneck, innovation and development of AI in pharmaceutical and chemical fields
With the birth of computer science in the early 21st century, mankind has entered a new era in terms of information storage. Early algorithms are mainly dedicated to liberating repetitive and tedious mathematical and logical operations. But in the following decades, the theoretical basis of related machine learning and various algorithms have been developed by leaps and bounds. Now the organic combination of big data, algorithms and computing power has contributed the application of artificial intelligence in many fields.
Chemistry is a classic discipline of natural sciences, which aims to study the composition, properties, structure and changing laws of matter at the molecular and atomic levels. Chemistry is closely related to human production and life, and plays a vital role in energy, materials, pharmaceuticals and other fields. Traditional chemical research and chemical production rely heavily on experiments guided by theory, requiring a lot of manpower and material resources, and largely depending on the experience and level of practitioners. Then how to apply cutting-edge results in the field of artificial intelligence to the field of chemistry to increase productivity has become an inevitable trend. The resulting cross-discipline of “AI + chemistry” also provides a wide range of exploration space for the vast number of scientific researchers and entrepreneurs.
A series of emerging topics need to be studied: the digitization of chemical molecular structure and physical and chemical properties, automatic prediction of chemical total synthesis or biosynthesis pathways, optimization of industrial production and purification processes of chemical compounds, high-throughput computational screening of drug molecules, drug molecules and proteins prediction and optimization of target binding, prediction of toxicology and metabolic process of drug molecules in vivo. Using artificial intelligence to assist the above-mentioned related topics can greatly improve R&D and production efficiency. Compared with the information technology industry, the application of artificial intelligence in the chemical field also encounters a series of challenges.
How artificial intelligence plays a role in the pharmaceutical and chemical fields?
AI models can cover more data and obtain information from more data. For example, deep learning models can provide better predictions and generate new molecular structures through the training and learning of big data, which was not possible for the previous classical scientific computing models. Another example: before the use of AI, hundreds of people may discuss together to develop a drug, and hundreds or thousands of molecules may be designed in the end; however, AI allows us to simulate millions of drugs at once as long as the model is appropriate.
Bottlenecks of applying AI in the chemical and pharmaceutical fields
The first bottleneck encountered is the data problem. In drug development application scenarios, it is difficult to automatically generate data. And without enough data, it may be impossible to do it unless sufficient data has been obtained. In addition, in drug development, many data do not have negative data, but as a machine learning model, negative samples are very important. Without this negative sample, the data is not balanced. This problem always exists and requires a large data system to support it.
The second bottleneck lies in artificial intelligence itself. Drug development is an application of artificial intelligence, and there are some limitations. Because the drug system itself is very complex, applying a cutting-edge thing to a complex system will create a bottleneck. For example, the learning and inheritance of the experience of pharmaceutical experts is difficult for artificial intelligence to handle.
MedAI is an AI drug R&D company that has successively launched a number of drug discovery 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.