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Found 5 papers, 1 papers with code
Extension of Transformational Machine Learning: Classification Problems
This study explores the application and performance of Transformational Machine Learning (TML) in drug discovery.
LGEM$^\text{+}$: a first-order logic framework for automated improvement of metabolic network models through abduction
The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation.
Beating the Best: Improving on AlphaFold2 at Protein Structure Prediction
We developed the ARStack PSP method by stacking AlphaFold2 and RoseTTAFold.
Transformative Machine Learning
In all three problems, transformative machine learning significantly outperforms the best intrinsic representation.
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 6 molecular representations, applied to more than 2, 700 QSAR problems.
