AIMC Topic: Drug Discovery

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ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches.

Journal of chemical information and modeling Nov 5, 2019
Adverse effects induced by drug-drug interactions may result in early termination of drug development or even withdrawal of drugs from the market, and many drug-drug interactions are caused by the inhibition of cytochrome P450 (CYP450) enzymes. There...
Artificial Intelligence Machine Learning Algorithms Humans Neural Networks, Computer Quantitative Structure-Activity Relationship Protein Isoforms Cytochrome P-450 Enzyme Inhibitors Drug Discovery Models, Biological Cytochrome P-450 Enzyme System
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System-level responses to cisplatin in pro-apoptotic stages of breast cancer MCF-7 cell line.

Computational biology and chemistry Nov 2, 2019
Cisplatin ceases cell division and induces apoptosis in cancer cell lines. It is well established that cisplatin alters the expression of many genes involved in several cellular processes and pathways including transcription, p53 signaling pathway, a...
Software Female Breast Neoplasms Humans Gene Ontology Protein Interaction Maps Cell Proliferation Drug Discovery Gene Regulatory Networks Apoptosis Antineoplastic Agents Gene Expression Regulation, Neoplastic Drug Screening Assays, Antitumor Neoplasm Proteins MCF-7 Cells Cisplatin
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Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models.

Journal of chemical information and modeling Oct 25, 2019
Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem...
Drug Discovery Deep Learning Models, Biological Quantitative Structure-Activity Relationship Online Systems Humans Software
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Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics.

Omics : a journal of integrative biology Oct 25, 2019
Pharmaceutical industry and the art and science of drug development are sorely in need of novel transformative technologies in the current age of digital health and artificial intelligence (AI). Often described as game-changing technologies, AI and m...
Artificial Intelligence Drug Repositioning Machine Learning Genomics Algorithms Computational Biology Reproducibility of Results Models, Theoretical Drug Discovery
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A Machine Learning-Based Prediction Platform for P-Glycoprotein Modulators and Its Validation by Molecular Docking.

Cells Oct 21, 2019
P-glycoprotein (P-gp) is an important determinant of multidrug resistance (MDR) because its overexpression is associated with increased efflux of various established chemotherapy drugs in many clinically resistant and refractory tumors. This leads to...
Machine Learning Software Molecular Docking Simulation Drug Discovery Humans ATP Binding Cassette Transporter, Subfamily B, Member 1 Artificial Intelligence
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Has Drug Design Augmented by Artificial Intelligence Become a Reality?

Trends in pharmacological sciences Oct 16, 2019
The application of artificial intelligence (AI) to drug discovery has become a hot topic in recent years. Generative molecular design based on deep learning is a particular an area of attention. Zhavoronkov et al. recently published a novel approach ...
Deep Learning Drug Design Drug Discovery Artificial Intelligence
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Graph Convolutional Neural Networks for Predicting Drug-Target Interactions.

Journal of chemical information and modeling Oct 16, 2019
Accurate determination of target-ligand interactions is crucial in the drug discovery process. In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions. First, we built an unsupervised graph-aut...
Proteins Ligands Databases, Chemical Drug Discovery Protein Binding Drug Development Protein Conformation Binding Sites Neural Networks, Computer
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Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction.

PloS one Oct 7, 2019
The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays. To date, the most common prediction strategies are based upon machine learning or other quantitative structure-act...
Cell Line Quantitative Structure-Activity Relationship Drug Resistance Software Small Molecule Libraries Humans Animals Machine Learning Drug Discovery
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Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library.

International journal of molecular sciences Sep 30, 2019
The constitutive androstane receptor (CAR) plays pivotal roles in drug-induced liver injury through the transcriptional regulation of drug-metabolizing enzymes and transporters. Thus, identifying regulatory factors for CAR activation is important for...
Ligands Algorithms Reproducibility of Results Quantitative Structure-Activity Relationship Drug Discovery Small Molecule Libraries Receptors, Cytoplasmic and Nuclear Deep Learning Constitutive Androstane Receptor
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All-Assay-Max2 pQSAR: Activity Predictions as Accurate as Four-Concentration ICs for 8558 Novartis Assays.

Journal of chemical information and modeling Sep 26, 2019
Profile-quantitative structure-activity relationship (pQSAR) is a massively multitask, two-step machine learning method with unprecedented scope, accuracy, and applicability domain. In step one, a "profile" of conventional single-assay random forest ...
Quantitative Structure-Activity Relationship Models, Chemical Biological Assay Inhibitory Concentration 50 Dose-Response Relationship, Drug Proteins Algorithms Drug Discovery Machine Learning Logistic Models
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