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+title: "What Can Uni-Mol Do Too? | Combining 3D Geometry and Text, Ushering in a New Era of Molecular Multimodal Learning"
+date: 2024-12-25
+categories:
+- Uni-Mol
+---
+
+With the increasing demand for molecular representation learning in drug discovery and materials design, multimodal representation learning that combines the 3D geometric structure of molecules with biomedical text is becoming a research hotspot. Recently, a paper titled "GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules" [1] published on arXiv introduced an innovative framework called GeomCLIP in detail. This framework combines the 3D geometric information of molecules with text descriptions and significantly enhances the multimodal learning ability of molecular representations through contrastive learning and denoising pre-training tasks, demonstrating superior performance in several downstream tasks.
+
+The research team developed a large-scale dataset called PubChem3D, which contains more than 200,000 pairs of 3D geometry and text descriptions of molecules, covering rich chemical and biological information. The GeomCLIP framework adopts a dual-encoder structure to encode the geometry and text of molecules respectively and aligns the representations of the two modalities through contrastive learning while retaining the ability of the geometric encoder to model the characteristics of 3D molecules. Experiments show that GeomCLIP has achieved excellent performance in molecular property prediction, molecule-to-text retrieval, and 3D molecular caption generation tasks, providing important support for drug research and development and materials design.
+
+This research was completed by a joint team from the Artificial Intelligence Research Laboratory at Pennsylvania State University and the Shenzhen International Graduate School of Tsinghua University. Teng Xiao from Pennsylvania State University is the first author and corresponding author of the paper, and the collaborators include Chao Cui, Huaisheng Zhu, and Professor Vasant G. Honava from Tsinghua University. This work has opened up a new direction for multimodal learning of molecular representations and also provided new solutions for drug discovery and materials science research.
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+
+## Research Background
+
+In recent years, molecular representation learning has played a crucial role in fields such as drug discovery and materials design. However, traditional molecular representation learning methods mainly rely on 1D SMILES sequences or 2D molecular graph structures. Although these methods can capture the basic characteristics of molecules to some extent, they often overlook the importance of the 3D geometric structure of molecules, which has a decisive influence on the physical and chemical properties of molecules. At the same time, the rich semantic information (such as molecular properties and substructures) contained in molecular text descriptions has not been fully utilized. Therefore, how to effectively combine the 3D geometric information of molecules with biomedical text has become a key challenge in the current field of molecular representation learning.
+
+Although there have been studies attempting to use the 3D geometric information of molecules for multimodal learning, many methods rely on approximate geometric structures generated from 1D sequences, which may have large errors and are difficult to accurately reflect the real state of molecules. In addition, these studies usually only use 3D geometric information as an auxiliary feature and fail to fully explore the deep alignment relationship between geometric and text information. To solve this problem, this paper proposes a new framework - GeomCLIP, which aligns the 3D geometric information and text representations of molecules through contrastive learning and denoising pre-training tasks, significantly improving the learning ability of molecular multimodal representations and providing a new tool for drug discovery and materials design.
+
+<center><img src=https://dp-public.oss-cn-beijing.aliyuncs.com/community/Blog%20Files/Uni-Mol_25_12_2024/pic1.webp pic_center width="70%" height="70%" /></center>
+
+*Figure 1. The overall framework of GeomCLIP, including the dual-encoder structure, the process of contrastive learning and denoising pre-training tasks, and how the model processes molecular geometry and text modalities.*
+
+## Method Introduction
+
+GeomCLIP is an innovative multimodal representation learning framework designed to align the 3D geometric structure of molecules with biomedical text and enhance the multimodal learning ability of molecular representations.
+
+In the GeomCLIP framework, Uni-Mol is used as the core geometric encoder to encode the 3D geometric structure of molecules and convert it into a high-dimensional feature representation for multimodal learning tasks. Specifically, the usage of Uni-Mol is as follows:
+
+1. **Input molecular 3D geometric information**: The Uni-Mol model takes atom types and 3D coordinates of atoms as input. By embedding atom types and using spatial position encoding to capture the geometric relationships between atoms, the model can initialize the representation of the 3D structure of the molecule.
+
+2. **Transformer-based encoding mechanism**: Uni-Mol adopts a Transformer architecture and learns the complex relationships between atoms in the 3D structure of the molecule through a multi-head self-attention mechanism. This mechanism allows the model to capture important chemical and geometric properties of the molecule.
+
+3. **Generate per-atom representation**: Through a multi-layer encoder, Uni-Mol generates a high-dimensional representation for each atom in the molecule, capturing the interaction between the atom and other atoms and its geometric environment.
+
+4. **Global molecular representation**: Through a pooling operation (such as global average pooling), Uni-Mol aggregates the per-atom representations into a global molecular representation as the final output of the geometric modality.
+
+5. **Cross-modal alignment**: The generated global molecular representation is input into the multimodal alignment mechanism of GeomCLIP to perform contrastive learning with the feature representation of the text modality. The high-quality geometric representation ability of Uni-Mol directly determines the alignment effect between the 3D geometric information of molecules and text semantic information.
+
+In this way, Uni-Mol becomes an indispensable part of the GeomCLIP framework, ensuring the accurate encoding of the 3D molecular structure and providing reliable geometric representation support for multimodal learning tasks.
+
+<center><img src=https://dp-public.oss-cn-beijing.aliyuncs.com/community/Blog%20Files/Uni-Mol_25_12_2024/pic2.webp pic_center width="70%" height="70%" /></center>
+
+*Figure 2. GeomCLIP framework: A dual-encoder architecture for molecular representation pre-training, including a geometric encoder (Uni-Mol) for processing 3D molecular structures and a text encoder for processing biomedical descriptions.*
+
+## Results and Discussion
+
+### A New Benchmark for the Performance of Quantum Chemical Property Prediction Tasks
+
+<center><img src=https://dp-public.oss-cn-beijing.aliyuncs.com/community/Blog%20Files/Uni-Mol_25_12_2024/pic3.webp pic_center width="70%" height="70%" /></center>
+
+Table 2 shows the performance of GeomCLIP in 12 quantum chemical property prediction tasks on the QM9 dataset and compares it with other models (such as 3D InfoMax, GraphMVP, MoleculeSDE, MoleculeJAE, 3D-MoLM, Uni-Mol, etc.). The tasks cover key quantum chemical properties such as polarizability (α), energy gradient (∇E), highest occupied molecular orbital energy (EHOMO), lowest unoccupied molecular orbital energy (ELUMO), dipole moment (µ), specific heat capacity (Cv). In all tasks, GeomCLIP performs excellently and significantly outperforms the comparison models, especially showing the best performance in key indicators such as EHOMO and ELUMO. This indicates that GeomCLIP has an excellent ability to align 3D geometric information with text semantics and can achieve higher accuracy in quantum chemical property prediction tasks.
+
+### A New Height in the Performance of Molecule-Text Retrieval Tasks
+
+<center><img src=https://dp-public.oss-cn-beijing.aliyuncs.com/community/Blog%20Files/Uni-Mol_25_12_2024/pic4.webp pic_center width="70%" height="70%" /></center>
+
+Table 3 shows the performance of GeomCLIP in the molecule-text retrieval task and compares it with other models (such as KV-PLM, MoMu, MolCA, 3D-MoLM, etc.). These tasks include two retrieval types: retrieving the corresponding text given a molecule (Molecule2Text) and retrieving the corresponding molecule given a text (Text2Molecule). The performance metrics include precision (Accuracy) and top-20 retrieval accuracy (R@20). The results show that GeomCLIP significantly outperforms the comparison models in both tasks, reaching a precision of 53.50% and R@20 of 85.53% in the Molecule2Text task, and a precision of 48.71% and R@20 of 83.50% in the Text2Molecule task.
+
+This result indicates that GeomCLIP has a significant advantage in aligning molecular geometry and text semantic representations and provides a powerful tool for molecule retrieval based on multimodal representations.
+
+### A New Breakthrough in the Accuracy of Molecule Caption Generation Tasks
+
+<center><img src=https://dp-public.oss-cn-beijing.aliyuncs.com/community/Blog%20Files/Uni-Mol_25_12_2024/pic5.webp pic_center width="70%" height="70%" /></center>
+
+Table 4 shows the performance of GeomCLIP in the molecule caption generation (Molecule Captioning) task on the PubChem3D dataset and compares it with other methods (such as MolT5, MoMu, MolCA, and 3D-MoLM). The evaluation metrics include BLEU-2, BLEU-4, ROUGE-1, ROUGE-2, and ROUGE-L. The results show that GeomCLIP achieves the best performance in all metrics, especially reaching 22.97 in BLEU-4 and 32.05 in ROUGE-L, significantly surpassing the second-best model 3D-MoLM's 22.19 in BLEU-4 and 28.75 in ROUGE-L.
+
+This result indicates that GeomCLIP can generate more accurate and semantically rich molecule descriptions after aligning the molecular geometric structure and text semantics, providing a new solution for molecule generation tasks in drug research and development and materials science.
+
+### Comprehensive Performance Verification of Multimodal Molecular Tasks
+
+<center><img src=https://dp-public.oss-cn-beijing.aliyuncs.com/community/Blog%20Files/Uni-Mol_25_12_2024/pic6.webp pic_center width="70%" height="70%" /></center>
+
+Table 5 shows the performance of GeomCLIP in the comparison of different models in the effectiveness between data distribution and performance in the contrast learning of 3D molecular structure and text modality. This table illustrates that the model, by combining the geometric encoder (such as Uni - Mol) with the text semantic model, has an alignment effect in multiple tasks. The model demonstrates performance advantages in tasks involving molecule caption generation, modality retrieval, and multimodal alignment, indicating the wide applicability of GeomCLIP in complex data modeling and multimodal molecular tasks.
+
+## Conclusion
+
+GeomCLIP proposes an innovative multimodal pre-training framework that realizes a major breakthrough in the field of molecular representation learning by aligning the 3D geometric information of molecules with text descriptions. In this framework, Uni - Mol, as the core geometric encoder, plays a crucial role. By capturing the complex relationships of the 3D structure of molecules, it provides high - precision geometric representation support for multimodal tasks. Combining contrastive learning and denoising pre-training tasks, GeomCLIP demonstrates excellent cross - modal alignment ability and significantly outperforms existing methods in molecular property prediction, molecule - text retrieval, and molecule caption generation tasks. In addition, the newly constructed PubChem3D dataset provides high - quality geometry - text pairs for research, laying a solid foundation for further exploring the collaborative learning of 3D geometry and text modalities.
+
+## Reference
+
+[1] https://arxiv.org/abs/2411.10821
+