Artificial intelligence (AI) is changing the field of chemistry in more and more ways, especially in areas like drug finding, data management, and making new chemicals. Adding AI technologies to these areas has made big progress, letting chemists explore new chemical spaces, improve the efficiency of drug creation, and make synthesis processes more efficient.
Applications of AI are changing the way processes are planned and carried out in chemical synthesis. For example, machine learning algorithms can pull out organic chemistry grammar from huge sets of chemical reactions. This makes it easier to make smart assistants that can learn on their own and offer ways to make things (Schwaller et al., 2021). “The Chemputer” is an example of this trend. It uses AI and smart automation to find new molecules and make current synthesis methods better by exploring experimental spaces and getting feedback in real time (Gromski et al., 2020). Also, robotic systems that use AI planning have shown that they can speed up flow synthesis processes, which means that they don’t need to rely on old reaction data and the production of organic compounds is more efficient (Coley et al., 2019).
The part AI plays in finding new drugs is also interesting. Researchers have found that deep learning can help predict molecular properties, find the best drug prospects, and even create new compounds from scratch (Heßler & Baringhaus, 2018; Abbas, 2024). AI-driven methods have been used to solve problems in structure-based drug discovery, which lets scientists guess protein structures and molecular bioactivity (Özçelik et al., 202 ). This is also important because there are so many possible chemical processes (Strieth-Kalthoff, 2024) that AI makes it easier to find different ways to make complex drug molecules. AI’s ability to look at and combine big datasets speeds up the drug development process and makes it more targeted and efficient.
However, adding AI to chemistry is not without problems, even with these improvements. One big problem is that AI models need high-quality data to be trained, which can be expensive. An important issue is that it can be hard to get complete reaction datasets, which can make AI less useful for predicting results and improving processes (Ghiandoni et al., 2019). Additionally, AI can create many synthetic pathways, but validating these pathways is still a difficult job that needs chemical experts to understand the basic rules (Schwaller et al., 2022). This shows how important it is for AI tools and human knowledge in the field of chemistry to work together.
Looking to the future, AI has a lot of room to grow in the field of chemistry. According to Chen (2024) and “Artificial Intelligence in Chemical Engineering: Past, Present, and Future Perspectives” (2023), as AI technologies continue to improve, they will make it easier to find new materials and drugs, make predictions more accurate, and make chemical processes more automated. As AI-powered platforms that can run experiments and analyze data on their own continue to be developed, they will likely change the way chemical research is done, making it more efficient and open to new ideas.
Using AI in chemistry is making it possible for big steps forward to be made in chemical synthesis and drug finding. Even though there are still problems, there is a huge amount of room for growth and change in these areas. This points to a future where AI is an important part of how chemical sciences develop.
References
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