add knn-dta and lpm24 workshop

_pages/about.md

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 <span class='anchor' id='about-me'></span>
 
-Qizhi Pei (裴启智 in Chinese) is currently a second year Ph.D. student at the [ALOHA group](https://aloha.show) of [Gaoling School of Artificial Intelligence (GSAI)](http://ai.ruc.edu.cn/) in [Renmin University of China (RUC)](https://www.ruc.edu.cn/), supervised by [Prof. Rui Yan](https://gsai.ruc.edu.cn/ruiyan). He got the B.S. degree from [School of Computer Science and Technology](https://cs.ustc.edu.cn/), [University of Science and Technology of China (USTC)](https://www.ustc.edu.cn/) in 2022. He currently is an intern of [Microsoft Research AI4Science](https://www.microsoft.com/en-us/research/lab/microsoft-research-ai4science/) and a member of [AI4Science Research Project](https://ai4sci-research.github.io), mentored by [Dr. Lijun Wu](https://apeterswu.github.io/). He also works closely with [Dr. Yingce Xia](https://www.microsoft.com/en-us/research/people/yinxia/) and [Kaiyuan Gao](https://github.com/KyGao).
+Qizhi Pei (裴启智 in Chinese) is currently a second year Ph.D. student at the [ALOHA group](https://aloha.show) of [Gaoling School of Artificial Intelligence (GSAI)](http://ai.ruc.edu.cn/) in [Renmin University of China (RUC)](https://www.ruc.edu.cn/), supervised by [Prof. Rui Yan](https://gsai.ruc.edu.cn/ruiyan). He got the B.S. degree from [School of Computer Science and Technology](https://cs.ustc.edu.cn/), [University of Science and Technology of China (USTC)](https://www.ustc.edu.cn/) in 2022. He was an intern of [Microsoft Research AI4Science](https://www.microsoft.com/en-us/research/lab/microsoft-research-ai4science/), mentored by [Dr. Lijun Wu](https://apeterswu.github.io/). He is also a member of [AI4Science Research Project](https://ai4sci-research.github.io). He also works closely with [Dr. Yingce Xia](https://www.microsoft.com/en-us/research/people/yinxia/) and [Kaiyuan Gao](https://github.com/KyGao).
 
 His researches focus on AI4science, geometric deep learning, and multi-modal learning for biomolecule. 
 
@@ -27,22 +27,27 @@ His researches focus on AI4science, geometric deep learning, and multi-modal lea
 
 
 # 🔥 News
-- *2024.06*: &nbsp; One paper about cross-modal learning for molecule is submitted to Arxiv.
-- *2024.05*: &nbsp;🎉🎉 BioT5+ is accepted by ACL 2024 (Findings). Thanks for all collaborators!
-- *2024.04*: &nbsp; One paper about docking is submitted to Arxiv.
-- *2024.02*: &nbsp; Two papers about cross-modal learning for biomolecule are submitted to Arxiv.
-- *2023.10*: &nbsp;🎉🎉 BioT5 is accepted by EMNLP 2023. Thanks for all collaborators!
-- *2023.10*: &nbsp;🎉🎉 SSM-DTA is accepted by Briefings in Bioinformatics 2023. Thanks for all collaborators!
-- *2023.09*: &nbsp;🎉🎉 FABind is accepted by NeurIPS 2023. Thanks for all collaborators!
+- *2024.07*: &nbsp;🎉🎉 The enhanced version of BioT5+ achieves remarkable results in [Language + Molecule @ ACL2024 Workshop/Competition](https://language-plus-molecules.github.io/#leaderboard): 
+  - 🥇 1st Place in the Text-based Molecule Generation Track.
+  - 🥈 2nd Place in the Molecular Captioning Track.
+  - Oral presentation in the workshop.
+- *2024.07*: &nbsp;🎉🎉 kNN-DTA is accepted by CIKM 2024. Thanks for all collaborators!
+- *2024.06*: &nbsp; [3D-MolT5](https://arxiv.org/abs/2406.05797) is submitted to Arxiv.
+- *2024.05*: &nbsp;🎉🎉 [BioT5+](https://arxiv.org/abs/2402.17810) is accepted by ACL 2024 (Findings). Thanks for all collaborators!
+- *2024.03*: &nbsp; [FABind+](https://arxiv.org/abs/2403.20261) is submitted to Arxiv.
+- *2024.03*: &nbsp; A [survey](https://arxiv.org/abs/2403.01528) about cross-modal learning for biomolecule is submitted to Arxiv.
+- *2023.10*: &nbsp;🎉🎉 [BioT5](https://arxiv.org/abs/2310.07276) is accepted by EMNLP 2023. Thanks for all collaborators!
+- *2023.10*: &nbsp;🎉🎉 [SSM-DTA](https://doi.org/10.1093/bib/bbad386) is accepted by Briefings in Bioinformatics 2023. Thanks for all collaborators!
+- *2023.09*: &nbsp;🎉🎉 [FABind](https://arxiv.org/pdf/2310.06763.pdf) is accepted by NeurIPS 2023. Thanks for all collaborators!
 
 # 📝 Publications
-<div class='paper-box'><div class='paper-box-image'><div><div class="badge">NeurIPS 2023</div><img src='images/fabind.png' alt="sym" width="100%"></div></div>
+<div class='paper-box'><div class='paper-box-image'><div><div class="badge">ACL 2024 (Findings)</div><img src='images/biot5_plus.png' alt="sym" width="100%"></div></div>
 <div class='paper-box-text' markdown="1">
 
-[FABind: Fast and Accurate Protein-Ligand Binding](https://arxiv.org/pdf/2310.06763.pdf) \\
-**Qizhi Pei**(co-first author), Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan
+[BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning](https://arxiv.org/abs/2402.17810) \\
+**Qizhi Pei**, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, Rui Yan
 
-[**Project**](https://neurips.cc/virtual/2023/poster/71739) \| [![](https://img.shields.io/github/stars/QizhiPei/FABind?style=social&label=Code+Stars)](https://github.com/QizhiPei/FABind) \| [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-blue?label=Model)](https://huggingface.co/QizhiPei/FABind_model/tree/main)
+[**Project**](https://github.com/QizhiPei/BioT5) \| [![](https://img.shields.io/github/stars/QizhiPei/BioT5?style=social&label=Code+Stars)](https://github.com/QizhiPei/BioT5) \| [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-blue?label=Model)](https://huggingface.co/QizhiPei/biot5-plus-base/tree/main) \| [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-blue?label=Data)](https://huggingface.co/datasets/QizhiPei/BioT5_finetune_dataset)
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@@ -56,13 +61,32 @@ His researches focus on AI4science, geometric deep learning, and multi-modal lea
 </div>
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-<div class='paper-box'><div class='paper-box-image'><div><div class="badge">ACL 2024 (Findings)</div><img src='images/biot5_plus.png' alt="sym" width="100%"></div></div>
+
+<div class='paper-box'><div class='paper-box-image'><div><div class="badge">NeurIPS 2023</div><img src='images/fabind.png' alt="sym" width="100%"></div></div>
 <div class='paper-box-text' markdown="1">
 
-[BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning](https://arxiv.org/abs/2402.17810) \\
-**Qizhi Pei**, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, Rui Yan
+[FABind: Fast and Accurate Protein-Ligand Binding](https://arxiv.org/pdf/2310.06763.pdf) \\
+**Qizhi Pei**(co-first author), Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan
+
+[**Project**](https://neurips.cc/virtual/2023/poster/71739) \| [![](https://img.shields.io/github/stars/QizhiPei/FABind?style=social&label=Code+Stars)](https://github.com/QizhiPei/FABind) \| [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-blue?label=Model)](https://huggingface.co/QizhiPei/FABind_model/tree/main)
+</div>
+</div>
+
+<div class='paper-box'><div class='paper-box-image'><div><div class="badge">Language + Molecules @ ACL 2024 Workshop (Oral)</div><img src='images/lpm24_workshop.png' alt="sym" width="100%"></div></div>
+<div class='paper-box-text' markdown="1">
+
+[Enhanced BioT5+ for Molecule-Text Translation: A Three-Stage Approach with Data Distillation, Diverse Training, and Voting Ensemble](https://language-plus-molecules.github.io/#paper) \\
+**Qizhi Pei**, Lijun Wu, Kaiyuan Gao, Jinhua Zhu, Rui Yan
+
+</div>
+</div>
+
+<div class='paper-box'><div class='paper-box-image'><div><div class="badge">CIKM 2024</div><img src='images/knn_dta.png' alt="sym" width="100%"></div></div>
+<div class='paper-box-text' markdown="1">
+
+[Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighbors](https://qizhipei.github.io/) \\
+**Qizhi Pei**(co-first author), Lijun Wu, Zhenyu He, Jinhua Zhu, Yingce Xia, Shufang Xie, Rui Yan
 
-[**Project**](https://github.com/QizhiPei/BioT5) \| [![](https://img.shields.io/github/stars/QizhiPei/BioT5?style=social&label=Code+Stars)](https://github.com/QizhiPei/BioT5)
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 </div>
 
@@ -76,6 +100,7 @@ His researches focus on AI4science, geometric deep learning, and multi-modal lea
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+# 📝 Preprints
 <div class='paper-box'><div class='paper-box-image'><div><div class="badge">Arxiv 2024</div><img src='images/3d_molt5.png' alt="sym" width="100%"></div></div>
 <div class='paper-box-text' markdown="1">