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Abstract
All the problems can be solved with the help of machines mainly computers using algorithm and by interpreting their output data is considered as artificial intelligence (AI). Artificial intelligence is faster than manual work, reduces manpower, more efficient and accurate and used in various field these days and coming up with more advanced technology. With the help of artificial intelligence, drugs can be formulated and produced in an advanced way. New machineries’ used in chemical or pharmaceutical labs are much advanced these days, that reduces the time of the analysis.
There is a strong bond between artificial intelligence and chemistry. In the field of chemistry designing new molecules, molecular property detection of molecules and compounds, drug discovery, synthesis and retrosynthesis of molecules, analysis prediction for better and accurate results, all these can be done with the help of artificial intelligence.
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References
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References
Trafton, A. (2020). Artificial intelligence yields new antibiotic, A deep-learning model identifies a powerful new drug that can kill many species of antibiotic-resistant bacteria, MIT News Office.
Bai, F., Hong, D., Lu, Y., Liu, H., Xu, C., & Yao, X. (2019). Prediction of the Antioxidant Response Elements’ Response of Compound by Deep Learning. Frontiers in Chemistry, 7. https://doi.org/10.3389/fchem.2019.00 385 DOI: https://doi.org/10.3389/fchem.2019.00385
Cancilla, J. C., Torrecilla, J. S., Proestos, C. V., & Valderrama, J. O. (2020). Editorial: Artificial Intelligence in Chemistry. Frontiers in Chemistry, 8. https://doi.org/10.3389/fchem.2020.00275 DOI: https://doi.org/10.3389/fchem.2020.00275
Gasteiger, J. (2020). Chemistry in Times of Artificial Intelligence. ChemPhysChem, 21(20), 2233–2242. https://doi.org/10.1002/cphc.202000518 DOI: https://doi.org/10.1002/cphc.202000518
Li, F., Wan, X., Xing, J., Tan, X., Li, X., Wang, Y., … Zheng, M. (2019). Deep Neural Network Classifier for Virtual Screening Inhibitors of (S)-Adenosyl-L-Methionine (SAM)-Dependent Methyltransferase Family. Frontiers in Chemistry, 7. https://doi.org/10.3389/fchem.2019.00324 DOI: https://doi.org/10.3389/fchem.2019.00324
Molina, J., Laroche, A., Richard, J.-V., Schuller, A.-S., & Rolando, C. (2019). Neural Networks Are Promising Tools for the Prediction of the Viscosity of Unsaturated Polyester Resins. Frontiers in Chemistry, 7. https://doi.org/10.3389/fchem.2019.00375 DOI: https://doi.org/10.3389/fchem.2019.00375
Panteleev, J., Gao, H., & Jia, L. (2018). Recent applications of machine learning in medicinal chemistry. Bioorganic & Medicinal Chemistry Letters, 28(17), 2807–2815. https://doi.org/10.1016/j.bmcl.2018.06.046 DOI: https://doi.org/10.1016/j.bmcl.2018.06.046
Trinh C, Meimaroglou D, Hoppe S.(2021). Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers. Processes, 9(8), 1456. https://doi.org/10.3390/pr9081456 DOI: https://doi.org/10.3390/pr9081456
Vega-Márquez, B., Nepomuceno-Chamorro, I., Jurado-Campos, N., & Rubio-Escudero, C. (2020). Deep Learning Techniques to Improve the Performance of Olive Oil Classification. Frontiers in Chemistry, 7. https://doi.org/10.3389/fchem.2019.00929 DOI: https://doi.org/10.3389/fchem.2019.00929
Zheng, S., Chang, W., Xu, W., Xu, Y., & Lin, F. (2019). e-Sweet: A Machine-Learning Based Platform for the Prediction of Sweetener and Its Relative Sweetness. Frontiers in Chemistry, 7. https://doi.org/10.3389/fchem.2019.00035 DOI: https://doi.org/10.3389/fchem.2019.00035