AIMC Topic: Drug Discovery

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The application of machine learning techniques to innovative antibacterial discovery and development.

Expert opinion on drug discovery Jun 17, 2020
INTRODUCTION: After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing preval...
Drug Development Drug Design Drug Discovery Machine Learning Algorithms Anti-Bacterial Agents Drug Resistance, Bacterial Animals Humans
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Insight into potent leads for alzheimer's disease by using several artificial intelligence algorithms.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie Jun 16, 2020
Several proteins including S-nitrosoglutathione reductase (GSNOR), complement Factor D, complement 3b (C3b) and Protein Kinase R-like Endoplasmic Reticulum Kinase (PERK), have been demonstrated to be involved in pathogenesis pathways for Alzheimer's ...
Quantitative Structure-Activity Relationship Humans Nootropic Agents Artificial Intelligence Alzheimer Disease Molecular Targeted Therapy Drug Discovery Plant Extracts Molecular Docking Simulation Cognition Computer-Aided Design Signal Transduction Drug Design Molecular Structure Brain Molecular Dynamics Simulation Animals
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Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding.

Journal of medicinal chemistry Jun 11, 2020
DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value. We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecul...
Neural Networks, Computer Drug Discovery Small Molecule Libraries Proto-Oncogene Proteins c-kit Estrogen Receptor alpha DNA Ligands Protein Kinase Inhibitors Epoxide Hydrolases
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Chemists: AI Is Here; Unite To Get the Benefits.

Journal of medicinal chemistry Jun 11, 2020
The latest developments in artificial intelligence (AI) have arrived into an existing state of creative tension between computational and medicinal chemists. At their most productive, medicinal and computational chemists have made significant progres...
Humans Chemistry, Pharmaceutical Cooperative Behavior Computational Chemistry Artificial Intelligence Drug Discovery Quantitative Structure-Activity Relationship
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Practical Applications of Deep Learning To Impute Heterogeneous Drug Discovery Data.

Journal of chemical information and modeling Jun 10, 2020
Contemporary deep learning approaches still struggle to bring a useful improvement in the field of drug discovery because of the challenges of sparse, noisy, and heterogeneous data that are typically encountered in this context. We use a state-of-the...
Quantitative Structure-Activity Relationship Chemistry, Pharmaceutical Drug Discovery Deep Learning
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The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis.

Molecules (Basel, Switzerland) Jun 4, 2020
Human ether-a-go-go-related gene (hERG) potassium channel blockage by small molecules may cause severe cardiac side effects. Thus, it is crucial to screen compounds for activity on the hERG channels early in the drug discovery process. In this study,...
Drug Discovery Machine Learning Humans Quantitative Structure-Activity Relationship Models, Molecular Algorithms Ether-A-Go-Go Potassium Channels Computer Simulation Potassium Channel Blockers
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Medicinal Chemists versus Machines Challenge: What Will It Take to Adopt and Advance Artificial Intelligence for Drug Discovery?

Journal of chemical information and modeling May 29, 2020
The field of artificial intelligence (AI) for generative chemistry is reaching the maturity stage, shifting the focus from the novelty of the algorithms to the quality of the generated molecules. To ensure continued evolution of AI technologies, we p...
Humans Artificial Intelligence Algorithms Drug Discovery Chemistry, Pharmaceutical
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TF3P: Three-Dimensional Force Fields Fingerprint Learned by Deep Capsular Network.

Journal of chemical information and modeling May 28, 2020
Molecular fingerprints are the workhorse in ligand-based drug discovery. In recent years, an increasing number of research papers reported fascinating results on using deep neural networks to learn 2D molecular representations as fingerprints. It is ...
Drug Discovery Neural Networks, Computer Machine Learning Ligands
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Using machine learning to improve ensemble docking for drug discovery.

Proteins May 25, 2020
Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking for virtual screening. Unfortunately, as there is no rigorous theory to connect the docking scores from multiple structures to measured activ...
Protein Kinase Inhibitors Binding Sites Molecular Docking Simulation Protein Structure, Secondary Drug Discovery Humans Bayes Theorem Protein Binding Ligands User-Computer Interface Support Vector Machine ErbB Receptors
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TOP: A deep mixture representation learning method for boosting molecular toxicity prediction.

Methods (San Diego, Calif.) May 21, 2020
At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a correspond...
Toxicity Tests Drug Discovery Pharmacology, Clinical Humans Cheminformatics Deep Learning Forecasting Datasets as Topic
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