If you have ever used Artificial Intelligence (AI) in your chemistry projects you should have concluded that it is going to change everything. It trains itself through machine learning techniques and neural networks. It mimics human intelligence features including reasoning, learning, perception, and natural language [1]. What great capabilities!
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Artificial Intelligence in Chemistry (image). Generated by Microsoft Copilot, 2024.
While it is not yet fully reliable, is considerably advantageous if some quality control measures can be applied. Baum et al. [2] describe AI as: “Artificial intelligence (AI) refers to the ability of machines to act in seemingly intelligent ways, making decisions in response to new inputs without being explicitly programmed to do so”.Many believe that AI is far from
being a mature tool for full-scale and high-capacity applications. An Editorial paper in Nature journal believes
“Machine-learning systems in chemistry need accurate and accessible training
data. Until they get it, they won’t achieve their potential” [3]. It believes the deficiency
of available chemical data to educate AI systems is the reason for this lag.
The superiority of AI predictions is associated with the quality of the data
used for its training. Therefore, providing such high-quality data sources is
necessary to develop AI to a more practical stage. In this procedure,
both experimental and simulated data and even the results of unsuccessful
experiments are necessary [3].
AI systems dealing with the
structure and properties of molecules need 5000-10000 data points to beat
conventional methods in answering chemical questions but, in most cases, even
5000 data points cannot be found [3]. AlphaFold
protein-structure-prediction tool which is a successful AI application in
chemistry, uses the protein’s structural information in the protein Data Bank,
started in 1971 and currently has more than 200,000 structures [3]. This further confirms how AI
systems are in need of access to enough reliable information. Open data
publication should help AI systems find more data to be trained as they use
only open data.
The subsequent points illustrate how AI has the potential to revolutionize the field of chemistry by achieving novel results that were previously unattainable [4]:
- Drug discovery via the prediction and optimising biological activity of compounds.
- Integrating data from various sources for more efficient research and conclusion.
- Automated laboratory platforms to minimise human role and improve precision and reliability.
- Connection of laboratory instruments to the Internet of Things (IoT) to perform data collection, monitoring, and analysis in real-time.
- Interpretation of complex spectroscopic data and establishing new analytical methods.
- Blockchain for the secure and transparent tracking of the chemical supply chain.
- Digital twins as the virtual replicas of chemical systems that are useful in process simulation, monitoring, and optimization.
- Virtual laboratories and augmented reality for teaching and education.
- Natural language processing to extract chemical literature.
- Predictive toxicology for safety enhancement.
- Environmental chemistry and sustainability.
- Molecular design and prediction of molecule properties.
- Smart control of chemical processes.
- Deep learning in structure-activity relationships to better correlate molecule structure and its biological activity.
- AI-driven high-throughput experimentation integrating AI and robotics to run parallel simultaneous experiments helping in high-volume research activities.
- Digital materials design and materials informatics to use data-based methods to obtain new materials with desired properties.
- Data-driven chemical reaction optimisation to predict reaction products.
- Automated synthesis planning to optimize synthesis reactions and minimise experimental work.
- Chemoinformatics and chemical data analysis to analyse large chemical data collections finding out sophisticated correlations between the chemical structure of molecules and their reactions.
- Quantum chemistry and simulations to increase the accuracy and efficiency of quantum chemical computations and molecular simulations.
Awesome! AI seems a very interesting niche of chemistry. If you like, you can try the following for a practical workout with your assistant, Chemist AI! It might promote to be a mentor or even a professor soon:
1. YesChat
2. Julius AI
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1. Cardoso Rial, R.: AI in analytical
chemistry: Advancements, challenges, and future directions. Talanta. 274,
125949 (2024). https://doi.org/10.1016/j.talanta.2024.125949
2. Baum, Z.J., Yu, X.,
Ayala, P.Y., Zhao, Y., Watkins, S.P., Zhou, Q.: Artificial Intelligence in
Chemistry: Current Trends and Future Directions. J. Chem. Inf. Model. 61,
3197–3212 (2021). https://doi.org/10.1021/acs.jcim.1c00619
3. For chemists, the AI
revolution has yet to happen. Nature. 617, 438–438 (2023).
https://doi.org/10.1038/d41586-023-01612-x
4. Ananikov, V.P.: Top 20
influential AI-based technologies in chemistry. Artif. Intell. Chem. 2, 100075
(2024). https://doi.org/10.1016/j.aichem.2024.100075
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