AIMC Topic: Drug Discovery

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Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction.

International journal of molecular sciences Jul 10, 2019
Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Followin...
Neural Networks, Computer Drug Discovery Deep Learning Software Reproducibility of Results
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Neural networks in drug discovery: current insights from medicinal chemists.

Future medicinal chemistry Jul 9, 2019
Humans Neural Networks, Computer Drug Discovery
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Deep Reinforcement Learning for Multiparameter Optimization in Drug Design.

Journal of chemical information and modeling Jul 5, 2019
In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods f...
Neural Networks, Computer Machine Learning Models, Theoretical Models, Molecular Molecular Structure Drug Design Drug Discovery
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Artificial Intelligence: A Novel Approach for Drug Discovery.

Trends in pharmacological sciences Jul 3, 2019
Molecular dynamics (MD) simulations can mechanistically explain receptor function. However, the enormous data sets that they may imply can be a hurdle. Plante and colleagues (Molecules, 2019) recently described a machine learning approach to the anal...
Artificial Intelligence Ligands Machine Learning Drug Discovery Molecular Dynamics Simulation
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Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance.

Journal of chemical information and modeling Jun 28, 2019
Drug-induced liver injury (DILI), one of the most common adverse effects, leads to drug development failure or withdrawal from the market in most cases, showing an emerging challenge that is to accurately predict DILI in the early stage. Recently, th...
Machine Learning Algorithms Reproducibility of Results Animals Computer Simulation Molecular Structure Rats Models, Biological Male Chemical and Drug Induced Liver Injury Rats, Sprague-Dawley Drug Discovery Vinblastine Structure-Activity Relationship Gene Expression Regulation
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Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout.

Journal of chemical information and modeling Jun 26, 2019
While the use of deep learning in drug discovery is gaining increasing attention, the lack of methods to compute reliable errors in prediction for Neural Networks prevents their application to guide decision making in domains where identifying unreli...
Drug Discovery Neural Networks, Computer Machine Learning
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DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences.

PLoS computational biology Jun 14, 2019
Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In severa...
Binding Sites Drug Discovery Amino Acid Sequence Proteins Deep Learning Ligands Computational Biology Models, Molecular Computer Simulation Sequence Analysis, Protein
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Deep Learning in Chemistry.

Journal of chemical information and modeling Jun 13, 2019
Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns represented in t...
Chemistry Techniques, Synthetic Drug Design Cheminformatics Drug Discovery Chemical Phenomena Deep Learning Humans Quantitative Structure-Activity Relationship Neural Networks, Computer
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A Structure-Based Drug Discovery Paradigm.

International journal of molecular sciences Jun 6, 2019
Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for fut...
Deep Learning Quantitative Structure-Activity Relationship Molecular Docking Simulation Drug Discovery
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Error Tolerance of Machine Learning Algorithms across Contemporary Biological Targets.

Molecules (Basel, Switzerland) Jun 4, 2019
Machine learning continues to make strident advances in the prediction of desired properties concerning drug development. Problematically, the efficacy of machine learning in these arenas is reliant upon highly accurate and abundant data. These two l...
Antineoplastic Agents ROC Curve Biomarkers, Tumor Drug Discovery Models, Biological Molecular Targeted Therapy Bayes Theorem Workflow Humans Neural Networks, Computer Algorithms Reproducibility of Results Machine Learning
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