Pages that link to "Q28611248"
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The following pages link to Activity, assay and target data curation and quality in the ChEMBL database (Q28611248):
Displaying 50 items.
- The ChEMBL database in 2017 (Q28584450) (← links)
- BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry (Q28591396) (← links)
- The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands (Q28603128) (← links)
- Privileged Structures Revisited (Q30101030) (← links)
- How Open Data Shapes In Silico Transporter Modeling (Q30491084) (← links)
- Retrieving GPCR data from public databases (Q31118373) (← links)
- Macromolecular target prediction by self-organizing feature maps (Q36229691) (← links)
- Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery. (Q36397674) (← links)
- bioassayR: Cross-Target Analysis of Small Molecule Bioactivity. (Q37671639) (← links)
- Toward Understanding the Cold, Hot, and Neutral Nature of Chinese Medicines Using in Silico Mode-of-Action Analysis (Q38930855) (← links)
- D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions (Q39328873) (← links)
- Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set. (Q42695674) (← links)
- PTS: a pharmaceutical target seeker. (Q47095525) (← links)
- Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling. (Q47933466) (← links)
- Ontological representation, integration, and analysis of LINCS cell line cells and their cellular responses. (Q49530386) (← links)
- The rise of deep learning in drug discovery (Q49794212) (← links)
- How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors. (Q54110122) (← links)
- Modeling Kinase Inhibition Using Highly Confident Data Sets. (Q54113536) (← links)
- Systematic search for benzimidazole compounds and derivatives with antileishmanial effects. (Q54214867) (← links)
- Beyond the Hype: Deep Neural Networks Outperform Established Methods Using A ChEMBL Bioactivity Benchmark Set (Q56915968) (← links)
- Web Resources for Discovery and Development of New Medicines (Q56973767) (← links)
- A large-scale dataset of in vivo pharmacology assay results (Q57799718) (← links)
- ChEMBL: towards direct deposition of bioassay data (Q58608307) (← links)
- Spotting and designing promiscuous ligands for drug discovery (Q61218042) (← links)
- Enhanced taxonomy annotation of antiviral activity data from ChEMBL (Q61797364) (← links)
- Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity (Q62488888) (← links)
- Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations (Q62719011) (← links)
- Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling (Q64139542) (← links)
- Advances and Challenges in Computational Target Prediction (Q64140566) (← links)
- A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds (Q89313647) (← links)
- Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values (Q90073685) (← links)
- Deep Learning in the Study of Protein-Related Interactions: Review (Q90188181) (← links)
- Rethinking drug design in the artificial intelligence era (Q91698273) (← links)
- Optimal Piecewise Linear Regression Algorithm for QSAR Modelling (Q91714290) (← links)
- Multi-channel PINN: investigating scalable and transferable neural networks for drug discovery (Q91758576) (← links)
- Accelerated Discovery of Potent Fusion Inhibitors for Respiratory Syncytial Virus (Q91803589) (← links)
- Accelerated Discovery of Novel Ponatinib Analogs with Improved Properties for the Treatment of Parkinson's Disease (Q91943574) (← links)
- Prioritization of novel ADPKD drug candidates from disease-stage specific gene expression profiles (Q92265492) (← links)
- Accessing public compound databases with KNIME (Q92335889) (← links)
- Investigation of Factors Affecting the Performance of in silico Volume Distribution QSAR Models for Human, Rat, Mouse, Dog & Monkey (Q92361861) (← links)
- Applications of deep learning for the analysis of medical data (Q92364530) (← links)
- Deep learning in drug discovery: opportunities, challenges and future prospects (Q92386364) (← links)
- Multi-targeted kinase inhibition alleviates mTOR inhibitor resistance in triple-negative breast cancer (Q92465982) (← links)
- Error Tolerance of Machine Learning Algorithms across Contemporary Biological Targets (Q92543978) (← links)
- Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool (Q92959613) (← links)
- Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account (Q93095309) (← links)
- Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors (Q93336669) (← links)
- Advances with support vector machines for novel drug discovery (Q93388067) (← links)
- Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker (Q95641606) (← links)
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions (Q104138396) (← links)