The impact of cross-docked poses on performance of machine learning classifier for protein-ligand binding pose prediction.
Chao ShenXueping HuJunbo GaoXujun ZhangHaiyang ZhongZhe WangLei XuYu KangDong-Sheng CaoTingjun HouPublished in: J. Cheminformatics (2021)
Keyphrases
- drug design
- machine learning
- protein secondary structure
- protein secondary structure prediction
- subcellular localization
- protein structure prediction
- pose invariant face recognition
- decision trees
- feature ranking
- learning classifier systems
- contact map
- mhc class ii
- drug discovery
- learning algorithm
- feature selection
- support vector machine
- virtual screening
- pose variations
- human pose
- protein protein interactions
- protein interaction
- hiv protease
- protein structure
- prediction accuracy
- predicting protein
- active learning
- protein classification
- protein protein
- training data
- pose estimation
- feature space
- feature set
- protein sequences
- training set
- ensemble classifier
- high throughput
- contact maps
- text mining
- d objects
- binding peptides
- pose space
- dna binding
- supervised learning
- amino acids
- experimentally determined
- computer vision
- machine learning algorithms
- binding sites
- statistical methods
- support vector
- action recognition