Target prediction utilising negative bioactivity data covering large chemical space
about
DRABAL: novel method to mine large high-throughput screening assays using Bayesian active learningTrisubstituted-Imidazoles Induce Apoptosis in Human Breast Cancer Cells by Targeting the Oncogenic PI3K/Akt/mTOR Signaling PathwayBIGCHEM: Challenges and Opportunities for Big Data Analysis in ChemistryExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomicsThe cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncologyDoes 'Big Data' exist in medicinal chemistry, and if so, how can it be harnessed?MOST: most-similar ligand based approach to target prediction.On the Integration of In Silico Drug Design Methods for Drug Repurposing.C-SPADE: a web-tool for interactive analysis and visualization of drug screening experiments through compound-specific bioactivity dendrograms.The advancement of multidimensional QSAR for novel drug discovery - where are we headed?VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization.Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set.Orthologue chemical space and its influence on target prediction.Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR.Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server.Computer-aided design of multi-target ligands at A1R, A2AR and PDE10A, key proteins in neurodegenerative diseases.Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory.Evidence-Based Precision Oncology with the Cancer Targetome.How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors.Extending in Silico Protein Target Prediction Models to Include Functional Effects.Systemic neurotransmitter responses to clinically approved and experimental neuropsychiatric drugsUsing Machine Learning to Predict Synergistic Antimalarial Compound Combinations With Novel StructuresPredicting Off-Target Binding Profiles With Confidence Using Conformal Prediction
P2860
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P2860
Target prediction utilising negative bioactivity data covering large chemical space
description
2015 nî lūn-bûn
@nan
2015 թուականին հրատարակուած գիտական յօդուած
@hyw
2015 թվականին հրատարակված գիտական հոդված
@hy
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
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name
Target prediction utilising negative bioactivity data covering large chemical space
@ast
Target prediction utilising negative bioactivity data covering large chemical space
@en
Target prediction utilising negative bioactivity data covering large chemical space
@nl
type
label
Target prediction utilising negative bioactivity data covering large chemical space
@ast
Target prediction utilising negative bioactivity data covering large chemical space
@en
Target prediction utilising negative bioactivity data covering large chemical space
@nl
prefLabel
Target prediction utilising negative bioactivity data covering large chemical space
@ast
Target prediction utilising negative bioactivity data covering large chemical space
@en
Target prediction utilising negative bioactivity data covering large chemical space
@nl
P2860
P50
P3181
P1476
Target prediction utilising negative bioactivity data covering large chemical space
@en
P2093
Avid M Afzal
P2860
P2888
P3181
P356
10.1186/S13321-015-0098-Y
P577
2015-01-01T00:00:00Z
P5875
P6179
1038668816