Summary of Achieving Well-informed Decision-making in Drug Discovery: a Comprehensive Calibration Study Using Neural Network-based Structure-activity Models, by Hannah Rosa Friesacher et al.
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models
by Hannah Rosa Friesacher, Ola Engkvist, Lewis Mervin, Yves Moreau, Adam Arany
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A novel computational approach for predicting drug-target interactions is presented in this study, which aims to accelerate the development of new therapeutic agents. The proposed Bayesian Linear Probing (BLP) method generates Hamiltonian Monte Carlo (HMC) trajectories to estimate uncertainty in neural network predictions, providing valuable information for optimal decision-making. To achieve well-calibrated models, different metrics including accuracy and calibration scores are compared for model hyperparameter tuning. The results demonstrate that BLP improves model calibration while achieving the performance of common uncertainty quantification methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps scientists develop new medicines faster by creating a reliable computer program to predict which drugs work best with specific targets in the body. By using special math and computer techniques, researchers can create more accurate predictions about how well different drugs will work. This is important because it can save time and money by helping scientists choose the most promising treatments. |
Keywords
» Artificial intelligence » Hyperparameter » Neural network
