AIMC Topic: Drug Discovery

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Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method.

Briefings in bioinformatics Sep 2, 2021
Accurate predictions of druggability and bioactivities of compounds are desirable to reduce the high cost and time of drug discovery. After more than five decades of continuing developments, quantitative structure-activity relationship (QSAR) methods...
Quantitative Structure-Activity Relationship Computer Graphics Computer Simulation Neural Networks, Computer Artificial Intelligence Drug Design Organic Chemicals Algorithms Drug Discovery Computational Biology Molecular Structure Models, Chemical
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Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction.

Briefings in bioinformatics Sep 2, 2021
Molecular descriptors are essential to not only quantitative structure activity/property relationship (QSAR/QSPR) models, but also machine learning based chemical and biological data analysis. In this paper, we propose persistent spectral hypergraph ...
Ligands Quantitative Structure-Activity Relationship Binding, Competitive Machine Learning Algorithms Drug Design Drug Discovery Humans Protein Binding Databases, Protein Computational Biology Proteins Models, Molecular Molecular Structure Pharmaceutical Preparations
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DeepDTAF: a deep learning method to predict protein-ligand binding affinity.

Briefings in bioinformatics Sep 2, 2021
Biomolecular recognition between ligand and protein plays an essential role in drug discovery and development. However, it is extremely time and resource consuming to determine the protein-ligand binding affinity by experiments. At present, many comp...
Protein Binding Proteins Models, Molecular Software Reproducibility of Results Drug Discovery Deep Learning Binding Sites Amino Acid Sequence Protein Conformation, alpha-Helical Ligands Data Accuracy Hydrogen Bonding
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Identification of active molecules against Mycobacterium tuberculosis through machine learning.

Briefings in bioinformatics Sep 2, 2021
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb) and it has been one of the top 10 causes of death globally. Drug-resistant tuberculosis (XDR-TB), extensively resistant to the commonly used first-line drugs, has e...
Support Vector Machine Tuberculosis, Multidrug-Resistant Neural Networks, Computer Area Under Curve Models, Biological Reproducibility of Results Humans Mycobacterium tuberculosis Drug Discovery Antitubercular Agents Data Accuracy ROC Curve
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A spatial-temporal gated attention module for molecular property prediction based on molecular geometry.

Briefings in bioinformatics Sep 2, 2021
MOTIVATION: Geometry-based properties and characteristics of drug molecules play an important role in drug development for virtual screening in computational chemistry. The 3D characteristics of molecules largely determine the properties of the drug ...
Molecular Conformation Protein Binding Computational Biology Proteins Models, Molecular Molecular Structure Pharmaceutical Preparations Ligands Algorithms Reproducibility of Results Drug Discovery Deep Learning Humans Neural Networks, Computer Software
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Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction.

Briefings in bioinformatics Sep 2, 2021
Structure-based virtual screenings (SBVSs) play an important role in drug discovery projects. However, it is still a challenge to accurately predict the binding affinity of an arbitrary molecule binds to a drug target and prioritize top ligands from ...
Molecular Docking Simulation Drug Discovery Deep Learning Protein Binding Humans Algorithms Drug Evaluation, Preclinical Protein Kinases Ligands Crystallization Databases, Protein Receptors, G-Protein-Coupled Molecular Structure
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PySmash: Python package and individual executable program for representative substructure generation and application.

Briefings in bioinformatics Sep 2, 2021
BACKGROUND: Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable phys...
Carcinogenicity Tests Drug Discovery Computational Biology Drug Screening Assays, Antitumor Carcinogens Humans Machine Learning Software
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Anticancer peptides prediction with deep representation learning features.

Briefings in bioinformatics Sep 2, 2021
Anticancer peptides constitute one of the most promising therapeutic agents for combating common human cancers. Using wet experiments to verify whether a peptide displays anticancer characteristics is time-consuming and costly. Hence, in this study, ...
Benchmarking Amino Acid Sequence Antineoplastic Agents Deep Learning Computational Biology Humans Neural Networks, Computer Computer Simulation Neoplasms Peptides Drug Discovery
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An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction.

Briefings in bioinformatics Sep 2, 2021
Accurately identifying potential drug-target interactions (DTIs) is a key step in drug discovery. Although many related experimental studies have been carried out for identifying DTIs in the past few decades, the biological experiment-based DTI ident...
Humans Machine Learning Drug Development Proteins Pharmaceutical Preparations Drug-Related Side Effects and Adverse Reactions Software Computer Simulation Drug Discovery
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An insight into artificial intelligence in drug discovery: an interview with Professor Gisbert Schneider.

Expert opinion on drug discovery Sep 1, 2021
Quantitative Structure-Activity Relationship Humans Artificial Intelligence Drug Discovery
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