AIMC Topic: Ligands

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Unveiling key drivers of hepatocellular carcinoma: a synergistic approach with network pharmacology, machine learning-driven ligand discovery and dynamic simulations.

SAR and QSAR in environmental research Jan 3, 2025
Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. This study aims to uncover the genes and pathways involved in HCC through network pharmacology (NP) and to discover potential drugs via machine learning (ML)-based lig...
Glycogen Synthase Kinase 3 beta Network Pharmacology Carcinoma, Hepatocellular Molecular Docking Simulation Proto-Oncogene Proteins c-akt Molecular Dynamics Simulation Drug Discovery Liver Neoplasms Ligands Quantitative Structure-Activity Relationship Humans Machine Learning
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PharmRL: pharmacophore elucidation with deep geometric reinforcement learning.

BMC biology Dec 31, 2024
BACKGROUND: Molecular interactions between proteins and their ligands are important for drug design. A pharmacophore consists of favorable molecular interactions in a protein binding site and can be utilized for virtual screening. Pharmacophores are ...
Binding Sites Pharmacophore Proteins Ligands Humans Algorithms Drug Design Deep Learning
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Evaluations of the Perturbation Resistance of the Deep-Learning-Based Ligand Conformation Optimization Algorithm.

Journal of chemical information and modeling Dec 26, 2024
In recent years, the deep learning (DL) technique has rapidly developed and shown great success in scoring the protein-ligand binding affinities. The protein-ligand conformation optimization based on DL-derived scoring functions holds broad applicati...
Protein Conformation Molecular Docking Simulation Deep Learning Protein Binding Proteins Ligands Algorithms
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BioStructNet: Structure-Based Network with Transfer Learning for Predicting Biocatalyst Functions.

Journal of chemical theory and computation Dec 20, 2024
Enzyme-substrate interactions are essential to both biological processes and industrial applications. Advanced machine learning techniques have significantly accelerated biocatalysis research, revolutionizing the prediction of biocatalytic activities...
Algorithms Deep Learning Molecular Dynamics Simulation Machine Learning Ligands Biocatalysis Enzymes
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Residue-Level Multiview Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors.

Journal of chemical information and modeling Dec 17, 2024
Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges d...
Humans Molecular Docking Simulation Drug Discovery Ligands Deep Learning Protein Kinase Inhibitors Adenosine Triphosphate Binding Sites
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Combined usage of ligand- and structure-based virtual screening in the artificial intelligence era.

European journal of medicinal chemistry Dec 10, 2024
Drug design has always been pursuing techniques with time- and cost-benefits. Virtual screening, generally classified as ligand-based (LBVS) and structure-based (SBVS) approaches, could identify active compounds in the large chemical library to reduc...
Small Molecule Libraries Humans Drug Evaluation, Preclinical Artificial Intelligence Machine Learning Ligands Drug Design Molecular Structure
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A Protein-Context Enhanced Master Slave Framework for Zero-Shot Drug Target Interaction Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
Drug Target Interaction (DTI) prediction plays a crucial role in in-silico drug discovery, especially for deep learning (DL) models. Along this line, existing methods usually first extract features from drugs and target proteins, and use drug-target ...
Proteins Databases, Protein Ligands Drug Discovery Humans Deep Learning Algorithms Computational Biology
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Intra-Inter Graph Representation Learning for Protein-Protein Binding Sites Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
Graph neural networks have drawn increasing attention and achieved remarkable progress recently due to their potential applications for a large amount of irregular data. It is a natural way to represent protein as a graph. In this work, we focus on p...
Neural Networks, Computer Machine Learning Ligands Computational Biology Binding Sites Proteins Databases, Protein Algorithms Protein Binding
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Structure-Aware Graph Attention Diffusion Network for Protein-Ligand Binding Affinity Prediction.

IEEE transactions on neural networks and learning systems Dec 2, 2024
Accurate prediction of protein-ligand binding affinities can significantly advance the development of drug discovery. Several graph neural network (GNN)-based methods learn representations of protein-ligand complexes via modeling intermolecule intera...
Humans Neural Networks, Computer Proteins Databases, Protein Algorithms Ligands Drug Discovery Protein Binding
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SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction.

Nature methods Nov 27, 2024
Accurately predicting protein-ligand interactions is crucial for understanding cellular processes. We introduce SurfDock, a deep-learning method that addresses this challenge by integrating protein sequence, three-dimensional structural graphs and su...
Humans Software Algorithms Proteins Molecular Docking Simulation Ligands Deep Learning Protein Conformation Protein Binding
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