Abstract
Artificial intelligence underwent remarkable advancement in the past decade, revolutionizing our way of thinking and unlocking unprecedented opportunities across various fields, including drug development. The emergence of large pretrained models, such as ChatGPT, has even begun to demonstrate human-level performance in certain tasks.
However, the difficulties of deploying and utilizing AI and pretrained model for nonexpert limited its practical use. To overcome this challenge, here we presented three highly accessible online tools based on a large pretrained model for chemistry, the Uni-Mol, for drug development against CNS diseases, including those targeting NMDA receptor: the blood–brain barrier (BBB) permeability prediction, the quantitative structure–activity relationship (QSAR) analysis system, and a versatile interface of the AI-based molecule generation model named VD-gen. We believe that these resources will effectively bridge the gap between cutting-edge AI technology and NMDAR experts, facilitating rapid and rational drug development.
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Acknowledgments
Zhenfeng Deng and Ruichu Gu contributed equally to this work. We would like to specially thank Guolin Ke, Zhifeng Gao, Shuqi Lu, and many other developers of Uni-Mol and VD-gen. Financial supports are gratefully acknowledged for the STI2030 Major Projects (2022ZD0212700).
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Deng, Z., Gu, R., Wen, H. (2024). Application of Deep Learning for Studying NMDA Receptors. In: Burnashev, N., Szepetowski, P. (eds) NMDA Receptors. Methods in Molecular Biology, vol 2799. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3830-9_16
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DOI: https://doi.org/10.1007/978-1-0716-3830-9_16
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