Edit StudentProjectForm: Protein Language Models for drug discovery

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Summary:	MANDATORY: (provide a one-line* summary of the project)*
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References:	Chen, J., Gu, Z., Xu, Y., Deng, M., Lai, L. and Pei, J., 2023. QuoteTarget: A sequence‐based transformer protein language model to identify potentially druggable protein targets. Protein Science, 32(2), p.e4555. Chen, L., Fan, Z., Chang, J., Yang, R., Hou, H., Guo, H., Zhang, Y., Yang, T., Zhou, C., Sui, Q. and Chen, Z., 2023. Sequence-based drug design as a concept in computational drug design. Nature Communications, 14(1), p.4217. Chen, D., Liu, J. and Wei, G.W., 2024. Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions. Nature Machine Intelligence, 6(7), pp.799-810. Jiang, J., Chen, L., Ke, L., Dou, B., Zhang, C., Feng, H., Zhu, Y., Qiu, H., Zhang, B. and Wei, G., 2024. A review of transformers in drug discovery and beyond. Journal of Pharmaceutical Analysis, p.101081. OPTIONAL: (Give 2-3 references for students to start with)
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Protein Language Models for drug discovery Traditional drug target identification is mainly carried out through biological experiments or high-throughput screening techniques, which often require a lot of time and money costs, so the number of drug molecules that can be screened is very limited. With the rapid development of science and technology, various DL and ML models, such as transformers, have emerged one after another, providing a new way for drug target identification. This greatly saves the cost of drug target identification and accelerates the process of drug research and development. The sequence-based transformer protein language model is an innovative tool for identifying potential drug targets. It can independently extract features from amino acid sequences for drug target protein identification, which has potential significance for identifying drug binding sites. References: Chen, J., Gu, Z., Xu, Y., Deng, M., Lai, L. and Pei, J., 2023. QuoteTarget: A sequence‐based transformer protein language model to identify potentially druggable protein targets. Protein Science, 32(2), p.e4555. Chen, L., Fan, Z., Chang, J., Yang, R., Hou, H., Guo, H., Zhang, Y., Yang, T., Zhou, C., Sui, Q. and Chen, Z., 2023. Sequence-based drug design as a concept in computational drug design. Nature Communications, 14(1), p.4217. Chen, D., Liu, J. and Wei, G.W., 2024. Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions. Nature Machine Intelligence, 6(7), pp.799-810. Jiang, J., Chen, L., Ke, L., Dou, B., Zhang, C., Feng, H., Zhu, Y., Qiu, H., Zhang, B. and Wei, G., 2024. A review of transformers in drug discovery and beyond. Journal of Pharmaceutical Analysis, p.101081.

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