AIMC Topic: Drug Discovery

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A novel artificial intelligence protocol for finding potential inhibitors of acute myeloid leukemia.

Journal of materials chemistry. B Feb 18, 2020
There is currently no effective treatment for acute myeloid leukemia, and surgery is also ineffective as an important treatment for most tumors. Rapidly developing artificial intelligence technology can be applied to different aspects of drug develop...
Humans Artificial Intelligence Leukemia, Myeloid, Acute Drug Discovery Machine Learning STAT3 Transcription Factor Protein Binding Ligands Molecular Dynamics Simulation Databases, Chemical
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Deep Learning-driven research for drug discovery: Tackling Malaria.

PLoS computational biology Feb 18, 2020
Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is ...
Reproducibility of Results Drug Discovery Deep Learning Humans Antimalarials Quantitative Structure-Activity Relationship Structure-Activity Relationship Malaria
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A Machine Learning Approach for Drug-target Interaction Prediction using Wrapper Feature Selection and Class Balancing.

Molecular informatics Feb 11, 2020
Drug-Target interaction (DTI) plays a crucial role in drug discovery, drug repositioning and understanding the drug side effects which helps to identify new therapeutic profiles for various diseases. However, the exponential growth in the genomic and...
Machine Learning Enzymes Ion Channels Receptors, G-Protein-Coupled Pharmaceutical Preparations Databases, Chemical Receptors, Cytoplasmic and Nuclear Amino Acid Sequence Algorithms Drug Development Databases, Protein Computer Simulation Drug Discovery
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Machine learning and ligand binding predictions: A review of data, methods, and obstacles.

Biochimica et biophysica acta. General subjects Feb 10, 2020
Computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data ...
Humans Machine Learning Algorithms Protein Binding Computational Biology Ligands Drug Discovery Big Data
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Image Based Liver Toxicity Prediction.

Journal of chemical information and modeling Feb 7, 2020
The drugs we use to cure our diseases can cause damage to the liver as it is the primary organ responsible for metabolism of environmental chemicals and drugs. To identify and eliminate potentially problematic drug candidates in the early stages of d...
Liver Algorithms Drug Discovery Neural Networks, Computer
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Drug-target interaction prediction with tree-ensemble learning and output space reconstruction.

BMC bioinformatics Feb 7, 2020
BACKGROUND: Computational prediction of drug-target interactions (DTI) is vital for drug discovery. The experimental identification of interactions between drugs and target proteins is very onerous. Modern technologies have mitigated the problem, lev...
Proteins Machine Learning Computer Simulation Drug Development Cluster Analysis Drug Discovery
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Exploring chemical space using natural language processing methodologies for drug discovery.

Drug discovery today Feb 3, 2020
Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge. Advances in natural language processing (NLP) methodologies in the processing of spoken languag...
Natural Language Processing Chemistry, Pharmaceutical Computer Simulation Drug Design Drug Discovery Humans
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A deep learning approach for the blind logP prediction in SAMPL6 challenge.

Journal of computer-aided molecular design Jan 30, 2020
Water octanol partition coefficient serves as a measure for the lipophilicity of a molecule and is important in the field of drug discovery. A novel method for computational prediction of logarithm of partition coefficient (logP) has been developed u...
Molecular Structure Models, Chemical Deep Learning Machine Learning Solubility Thermodynamics Water Octanols Drug Discovery
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Costless Performance Improvement in Machine Learning for Graph-Based Molecular Analysis.

Journal of chemical information and modeling Jan 28, 2020
Graph neural networks (GNNs) have attracted significant attention from the chemical science community because molecules can be represented as a featured graph. In particular, graph convolutional network (GCN) and its variants have been widely used an...
Neural Networks, Computer Machine Learning Drug Discovery
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Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction.

Journal of computer-aided molecular design Jan 20, 2020
Machine learning methods may have the potential to significantly accelerate drug discovery. However, the increasing rate of new methodological approaches being published in the literature raises the fundamental question of how models should be benchm...
Benchmarking Algorithms Drug Evaluation, Preclinical Computer Simulation ROC Curve Drug Discovery Humans Support Vector Machine Deep Learning Machine Learning Area Under Curve User-Computer Interface
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