Predicting ligand binding modes from neural networks trained on protein-ligand interaction fingerprints.
about
Delineation of Polypharmacology across the Human Structural Kinome Using a Functional Site Interaction Fingerprint ApproachImprovements, trends, and new ideas in molecular docking: 2012-2013 in review.The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions.Protein-Ligand Scoring with Convolutional Neural Networks.A D3R prospective evaluation of machine learning for protein-ligand scoring.A web server for analysis, comparison and prediction of protein ligand binding sitesSimple Ligand-Receptor Interaction Descriptor (SILIRID) for alignment-free binding site comparison.Prediction of sensitivity to gefitinib/erlotinib for EGFR mutations in NSCLC based on structural interaction fingerprints and multilinear principal component analysis.Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates.
P2860
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P2860
Predicting ligand binding modes from neural networks trained on protein-ligand interaction fingerprints.
description
2013 nî lūn-bûn
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2013年の論文
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2013年学术文章
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2013年学术文章
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name
Predicting ligand binding mode ...... gand interaction fingerprints.
@en
Predicting ligand binding mode ...... gand interaction fingerprints.
@nl
type
label
Predicting ligand binding mode ...... gand interaction fingerprints.
@en
Predicting ligand binding mode ...... gand interaction fingerprints.
@nl
prefLabel
Predicting ligand binding mode ...... gand interaction fingerprints.
@en
Predicting ligand binding mode ...... gand interaction fingerprints.
@nl
P50
P356
P1476
Predicting ligand binding mode ...... gand interaction fingerprints.
@en
P2093
Alexandre Varnek
P304
P356
10.1021/CI300200R
P577
2013-03-29T00:00:00Z