Artificial Intelligence in Chemistry: Accelerating Drug and Materials Discovery

Artificial Intelligence (AI) has emerged as a powerful tool in modern chemical research, significantly accelerating the discovery and development of new drugs and advanced materials. Traditional experimental approaches in chemistry are often time-consuming, expensive, and require extensive trial-and-error processes. AI techniques, particularly machine learning and deep learning, enable researchers to analyse large chemical datasets, predict molecular properties, and identify potential compounds with greater efficiency and accuracy. In drug discovery, AI helps in predicting drug–target interactions, optimizing molecular structures, and reducing the time required for lead identification. Similarly, in materials science, AI assists in designing novel materials with desired properties for applications in energy storage, catalysis, and electronics.

This paper highlights the role of AI-driven computational models in transforming chemical research, discusses key methodologies, and examines current challenges and future prospects. The integration of artificial intelligence with chemical sciences is expected to revolutionize drug development and materials innovation, making the discovery process faster, more cost-effective, and highly efficient.

Artificial Intelligence (AI), Machine Learning, Deep Learning, Drug Discovery, Materials Discovery.

1. Burki, T. (2019). Pharma blockchains AI for drug development. The Lancet, 393, 2382. https://doi.org/10.1016/S0140-6736(19)31160-8 [Google Scholar] [Crossref]

2. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559(7715), 547–555. https://doi.org/10.1038/s41586-018-0337-2 [Google Scholar] [Crossref]

3. Chakravarty, K., Antontsev, V. G., Khotimchenko, M., et al. (2021). Accelerated repurposing and drug development of pulmonary hypertension therapies for COVID-19 treatment using an AI-integrated biosimulation platform. Molecules, 26(7), 1912. https://doi.org/10.3390/molecules26071912 [Google Scholar] [Crossref]

4. Gallego, V., Naveiro, R., Roca, C., Ríos Insua, D., & Campillo, N. E. (2021). AI in drug development: A multidisciplinary perspective. Molecular Diversity, 25, 1461–1479. https://doi.org/10.1007/s11030-021-10213-7 [Google Scholar] [Crossref]

5. Hao, Y., Lynch, K., Fan, P., et al. (2023). Development of a machine learning algorithm for drug screening analysis on high-resolution UPLC-MSE/QTOF mass spectrometry. Journal of Applied Laboratory Medicine, 8(1), 53–66. https://doi.org/10.1093/jalm/jfac119 [Google Scholar] [Crossref]

6. Hong, E., Jeon, J., & Kim, H. U. (2023). Recent development of machine learning models for the prediction of drug–drug interactions. Korean Journal of Chemical Engineering, 40, 276–285. https://doi.org/10.1007/s11814-022-1285-4 [Google Scholar] [Crossref]

7. Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020). Drug discovery with explainable artificial intelligence. Nature Machine Intelligence, 2(10), 573–584. https://doi.org/10.1038/s42256-020-00236-4 [Google Scholar] [Crossref]

8. Liu, Z., Roberts, R. A., Lal-Nag, M., Chen, X., Huang, R., & Tong, W. (2021). AI-based language models powering drug discovery and development. Drug Discovery Today, 26(11), 2593–2607. https://doi.org/10.1016/j.drudis.2021.06.003 [Google Scholar] [Crossref]

9. Sarkar, C., Das, B., Rawat, V. S., et al. (2023). Artificial intelligence and machine learning technology driven modern drug discovery and development. International Journal of Molecular Sciences, 24(3), 2026. https://doi.org/10.3390/ijms24032026 [Google Scholar] [Crossref]

10. Schneider, G. (2018). Automating drug discovery. Nature Reviews Drug Discovery, 17(2), 97–113. https://doi.org/10.1038/nrd.2017.232 [Google Scholar] [Crossref]

11. Stokes, J. M., Yang, K., Swanson, K., et al. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688–702. https://doi.org/10.1016/j.cell.2020.01.021 [Google Scholar] [Crossref]

12. Vamathevan, J., Clark, D., Czodrowski, P., et al. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477. https://doi.org/10.1038/s41573-019-0024-5 [Google Scholar] [Crossref]

13. Wang, Y., Xiong, H., & Liu, Z. (2020). Deep learning for drug discovery and development. Journal of Chemical Information and Modeling, 60(12), 5697–5712. https://doi.org/10.1021/acs.jcim.0c00932 [Google Scholar] [Crossref]

14. Zunger, A. (2018). Inverse design in search of materials with target functionalities. Nature Reviews Chemistry, 2, 0121. https://doi.org/10.1038/s41570-018-0121 [Google Scholar] [Crossref]

• Investigating the Performance of Colour Oxide on the PS/CNSO Oil Paint Production
• Real-Time Fabric Defect Detection Using a Lightweight Deformable YOLO Network
• Recent Trends in the Stock Market: An Analytical Study of Market Dynamics, Investor Behaviour, and Technological Influence
• Evaluation of Structural Dynamics and Equilibrium State, with Case Study of Large Cantilever Projection for a 10- Storey Reinforced Concrete Building in Lagos, Nigeria.
• Multi-Criteria Evaluation of AI-Based Adaptive Learning Platforms in Global Higher Education: A Fuzzy AHP Perspective