Summary of Advancements in Molecular Property Prediction: a Survey Of Single and Multimodal Approaches, by Tanya Liyaqat et al.
Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches
by Tanya Liyaqat, Tanvir Ahmad, Chandni Saxena
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Materials Science (cond-mat.mtrl-sci); Chemical Physics (physics.chem-ph); Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in Molecular Property Prediction (MPP) have been remarkable, driven by exponential growth in chemical data and artificial intelligence (AI). However, effectively representing molecular data remains a challenge. To address this, representation learning techniques are crucial for acquiring informative and interpretable representations of molecular structures, SMILES notation, and images. This paper explores AI-based approaches in MPP, focusing on single and multiple modality representation techniques. It discusses various molecule representations and encoding schemes, categorizes MPP methods by their use of modalities, and outlines datasets and tools available for feature generation. The performance of recent methods is analyzed, and future research directions are suggested to advance the field of MPP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about predicting properties of molecules using artificial intelligence (AI). This is important because it helps us discover new medicines, materials, and chemicals. But there’s a problem: we need to find ways to represent molecular data in a way that AI can understand. The paper looks at different techniques for doing this, including single and multiple methods. It also talks about the types of molecules and how they’re represented, as well as the tools and datasets used. Overall, the goal is to improve our ability to predict properties of molecules using AI. |
Keywords
* Artificial intelligence * Representation learning
