Summary of Del-ranking: Ranking-correction Denoising Framework For Elucidating Molecular Affinities in Dna-encoded Libraries, by Hanqun Cao et al.
DEL-Ranking: Ranking-Correction Denoising Framework for Elucidating Molecular Affinities in DNA-Encoded Libraries
by Hanqun Cao, Mutian He, Ning Ma, Chang-yu Hsieh, Chunbin Gu, Pheng-Ann Heng
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel distribution-correction denoising framework, DEL-Ranking, is introduced to address the challenges of noise in read counts during DNA-encoded library (DEL) screening. The approach employs a ranking loss that rectifies relative magnitude relationships between read counts and an iterative algorithm using self-training and consistency loss to establish model coherence. Additionally, three new DEL screening datasets are contributed, including multi-dimensional molecular representations, protein-ligand enrichment values, and activity labels. These datasets mitigate data scarcity issues in AI-driven DEL screening research. The framework demonstrates superior performance across multiple correlation metrics and zero-shot generalization ability across different protein targets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DEL-Ranking is a new way to analyze DNA-encoded library (DEL) screenings. It helps remove noise from the results, which makes it easier to find important patterns. The approach uses two main ideas: a special loss function that fixes relationships between read counts and an algorithm that trains the model to be consistent. To test the framework, three new datasets were created that include more information about the molecules and how they interact with proteins. These datasets help solve a problem called data scarcity in DEL screening research. The results show that DEL-Ranking works better than other methods and can even predict binding affinity without seeing it before. |
Keywords
* Artificial intelligence * Generalization * Loss function * Self training * Zero shot
