AIMC Topic: Ligands

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In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm.

Molecular diversity Feb 7, 2021
Aptamers can be regarded as efficient substitutes for monoclonal antibodies in many diagnostic and therapeutic applications. Due to the tedious and prohibitive nature of SELEX (systematic evolution of ligands by exponential enrichment), the in silico...
Machine Learning Molecular Docking Simulation Drug Design Aptamers, Nucleotide Algorithms Biomarkers CD13 Antigens Proteins Molecular Dynamics Simulation Ligands Molecular Conformation RNA
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New machine learning and physics-based scoring functions for drug discovery.

Scientific reports Feb 4, 2021
Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different tar...
Internet Ligands Hydrophobic and Hydrophilic Interactions Drug Discovery Support Vector Machine Entropy Protein Interaction Mapping Drugs, Investigational Protease Inhibitors Peptide Hydrolases Datasets as Topic Humans Research Design Molecular Docking Simulation Software
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SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.

International journal of molecular sciences Jan 30, 2021
Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently show...
Ligands Humans Datasets as Topic Caenorhabditis elegans Binding Sites Animals Protein Structure, Secondary Deep Learning Protein Binding Drug Discovery Protein Domains
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Systematic analysis of binding of transcription factors to noncoding variants.

Nature Jan 27, 2021
Many sequence variants have been linked to complex human traits and diseases, but deciphering their biological functions remains challenging, as most of them reside in noncoding DNA. Here we have systematically assessed the binding of 270 human trans...
Ligands Genome, Human Polymorphism, Single Nucleotide Binding Sites Transcription Factors SELEX Aptamer Technique Disease Support Vector Machine Humans Protein Binding
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Using Domain-Specific Fingerprints Generated Through Neural Networks to Enhance Ligand-Based Virtual Screening.

Journal of chemical information and modeling Jan 26, 2021
Similarity-based virtual screening is a fundamental tool in the early drug discovery process and relies heavily on molecular fingerprints. We propose a novel strategy of generating domain-specific fingerprints by training neural networks on target-sp...
Molecular Structure Ligands Neural Networks, Computer Drug Discovery
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Accelerating Drug Design against Novel Proteins Using Deep Learning.

Journal of chemical information and modeling Jan 25, 2021
In the world plagued by the emergence of new diseases, it is essential that we accelerate the drug design process to develop new therapeutics against them. In recent years, deep learning-based methods have shown some success in ligand-based drug desi...
Drug Design Deep Learning Ligands Catalytic Domain Proteins Humans
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Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists.

Journal of chemical information and modeling Jan 6, 2021
The α2a adrenoceptor is a medically relevant subtype of the G protein-coupled receptor family. Unfortunately, high-throughput techniques aimed at producing novel drug leads for this receptor have been largely unsuccessful because of the complex pharm...
Receptors, Adrenergic, alpha-2 Medetomidine Deep Learning Ligands
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Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes.

Proteins Dec 31, 2020
Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to di...
Neural Networks, Computer Machine Learning Proteins Ligands Models, Molecular Protein Conformation Rotation
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A computational model for GPCR-ligand interaction prediction.

Journal of integrative bioinformatics Dec 29, 2020
G protein-coupled receptors (GPCRs) play an essential role in critical human activities, and they are considered targets for a wide range of drugs. Accordingly, based on these crucial roles, GPCRs are mainly considered and focused on pharmaceutical r...
Ligands Algorithms Humans Support Vector Machine Machine Learning Bayes Theorem
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Machine Learning of Allosteric Effects: The Analysis of Ligand-Induced Dynamics to Predict Functional Effects in TRAP1.

The journal of physical chemistry. B Dec 28, 2020
Allosteric molecules provide a powerful means to modulate protein function. However, the effect of such ligands on distal orthosteric sites cannot be easily described by classical docking methods. Here, we applied machine learning (ML) approaches to ...
Allosteric Site Machine Learning Allosteric Regulation Ligands Molecular Dynamics Simulation Molecular Chaperones
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