The Daily Bulletin

The Chemistry Department is hosting a graduate student seminar on Thursday, February 27, during Bengal Pause (12:15 to 1:30 p.m.) in SAMC 151. Our speaker will be Alyssa Heisler, a graduate student in the forensic science master's program. Coffee and snacks will be served.

Abstract

            The development of small molecules targeting immune checkpoint proteins, such as cytotoxic T-lymphocyte associated protein 4 (CTLA-4), has revolutionized cancer immunotherapy. CTLA-4, which is an inhibitory receptor on T cells, plays a critical role in downregulating immune responses, and the blockade or inhibition has been proven to be effective in enhancing anti-tumor immunity.1 However, the discovery of these small molecules that specifically inhibit CTLA-4 remains a complex and intensive process. Recent advancements in Artificial Intelligence (AI) have significantly impacted the field of drug discovery, particularly in the search for small molecules targeting these immune checkpoint proteins. AI techniques, such as machine learning (ML), deep learning, and predictive modeling are transforming the landscape of drug discovery by enabling faster, more efficient identification and optimization of promising therapeutic drug candidates. AI models can rapidly analyze large datasets that include chemical libraries, protein structures, biological assays, and can predict molecular interactions with target proteins like CTLA-4.2 AI algorithms are advantageous in screening vast chemical spaces and identifying lead compounds that would otherwise be difficult to discover through conventional methods. The ability of AI to integrate diverse types of data provides a deeper understanding of how these inhibitors may interact with the immune system and with the molecular mechanisms underlying CTLA-4 inhibition, therefore, paving the way for more targeted and personalized therapies.3 As AI technologies continue to evolve, their role in drug discovery will likely expand, providing opportunities to accelerate the development of novel small molecule therapies for cancer immunotherapy and in drug discovery overall.

References

Sobhani, N.; Tardiel-Cyril, D. R.; Chai, D.; Generali, D.; Li, J.-R.; Vazquez-Perez, J.; Lim, J. M.;.Morris, R.; Bullock, Z. N.; Davtyan, A.; Cheng, C.; Decker, W. K.; Li, Y. Artificial intelligence-powered discovery of small molecules inhibiting CTLA-4 in cancer. BJC Rep 2024, 2, 4. https://doi.org/10.1038/s44276-023-00035-5
Sarkar, C.; Das, B.; Rawat, V.S.; Wahlang, J.B.; Nongpiur, A.; Tiewsoh, I.; Lyngdoh, N.M.; Das, D.; Bidarolli, M.; Sony, H.T. Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development. Int. J. Mol. Sci. 2023, 24, 2026. https://doi.org/10.3390/ijms24032026
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. Artificial Intelligence in drug discovery and development. Drug Discov. Today. 2021, 26, 80-93. doi: 10.1016/j.drudis.2020.10.010.