AIMC Topic: Drug Design

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Machine-Learning Prediction of Drug-Induced Cardiac Arrhythmia: Analysis of Gene Expression and Clustering.

Critical reviews in biomedical engineering Jan 1, 2018
A marked delay in the electrical repolarization of heart ventricles is characterized by prolongation of the Q-T wave (QT) interval on a surface electrocardiogram. Such a delay can lead to potentially life-threatening cardiac arrhythmia (torsades de p...
MAP Kinase Signaling System Gene Expression Regulation Clinical Trials as Topic Drug Evaluation, Preclinical Heart Ventricles Arrhythmias, Cardiac False Positive Reactions Torsades de Pointes Rats Electrocardiography Support Vector Machine Cluster Analysis Signal Processing, Computer-Assisted Heart Drug Design Gene Expression Profiling Mice Sensitivity and Specificity Animals Male Machine Learning Female Reproducibility of Results Humans
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Machine Learning-Based Modeling of Drug Toxicity.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2018
Toxicity is an important reason for the failure of drug research and development (R&D). The traditional experimental testings for chemical toxicity profile are costly and time-consuming. Therefore, it is attractive to develop the effective and accura...
Machine Learning Software Drug Design Humans Ligands Quantitative Structure-Activity Relationship Sequence Homology, Amino Acid Ether-A-Go-Go Potassium Channels Pharmaceutical Preparations Models, Biological Carcinogenesis Drug-Related Side Effects and Adverse Reactions Molecular Structure Internet
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Generative Models for Artificially-intelligent Molecular Design.

Molecular informatics Jan 1, 2018
Drug Discovery Neural Networks, Computer Drug Design
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Cheminformatics and the Mean.

Molecular informatics Jan 1, 2018
Chemistry, Pharmaceutical Artificial Intelligence Drug Design
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Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

Combinatorial chemistry & high throughput screening Jan 1, 2018
AIM AND OBJECTIVE: Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug d...
Humans Artificial Intelligence Least-Squares Analysis Pharmaceutical Preparations Binding Sites Algorithms Drug Design Blood Proteins
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DeepSite: protein-binding site predictor using 3D-convolutional neural networks.

Bioinformatics (Oxford, England) Oct 1, 2017
MOTIVATION: An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years b...
Protein Conformation Neural Networks, Computer Algorithms Binding Sites Machine Learning Drug Design Software Proteins
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The drug-maker's guide to the galaxy.

Nature Sep 26, 2017
Machine Learning Algorithms Computer Simulation Drug Design Humans Drug Discovery Databases, Chemical
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Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

Current drug discovery technologies Jan 1, 2017
BACKGROUND: Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and exc...
Least-Squares Analysis Pharmaceutical Preparations Models, Biological Intestinal Absorption Humans Artificial Intelligence Drug Design
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Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming.

IEEE/ACM transactions on computational biology and bioinformatics Jan 1, 2017
Growing bacterial resistance to antibiotics is spurring research on utilizing naturally-occurring antimicrobial peptides (AMPs) as templates for novel drug design. While experimentalists mainly focus on systematic point mutations to measure the effec...
Gram-Positive Bacteria Genetic Engineering Decision Trees Antimicrobial Cationic Peptides Gram-Negative Bacteria Algorithms Neural Networks, Computer Drug Design Computational Biology Machine Learning
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Cheminformatics Based Machine Learning Approaches for Assessing Glycolytic Pathway Antagonists of Mycobacterium tuberculosis.

Combinatorial chemistry & high throughput screening Jan 1, 2016
BACKGROUND: Tuberculosis is the second leading cause of death from an infectious disease worldwide after HIV, thus reasoning the expeditions in antituberculosis research. The rising number of cases of infection by resistant forms of M. tuberculosis h...
Computational Biology Tuberculosis Mycobacterium tuberculosis Algorithms Humans High-Throughput Screening Assays Drug Design Machine Learning Enzyme Inhibitors Glycolysis Fructose-Bisphosphate Aldolase
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