AIMC Topic: Ligands

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Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method.

Journal of computer-aided molecular design Mar 17, 2020
Scoring functions are routinely deployed in structure-based drug design to quantify the potential for protein-ligand (PL) complex formation. Here, we present a new scoring function Bappl+ that is designed to predict the binding affinities of non-meta...
Machine Learning Drug Design Ligands Proteins Metals Databases, Protein Protein Binding Quantum Theory Computational Biology
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Energy-based graph convolutional networks for scoring protein docking models.

Proteins Mar 16, 2020
Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative for su...
Deep Learning Protein Binding Proteins Research Design Peptides Molecular Docking Simulation Binding Sites Thermodynamics Computer Graphics Benchmarking Amino Acid Sequence Ligands Humans Software Structural Homology, Protein Protein Interaction Mapping Protein Conformation, alpha-Helical Protein Multimerization Protein Conformation, beta-Strand Protein Interaction Domains and Motifs
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Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition.

Biomolecules Mar 14, 2020
We show that machine learning can pinpoint features distinguishing inactive from active states in proteins, in particular identifying key ligand binding site flexibility transitions in GPCRs that are triggered by biologically active ligands. Our anal...
Ligands Humans Machine Learning Protein Binding Receptors, G-Protein-Coupled Protein Domains
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Machine learning models for drug-target interactions: current knowledge and future directions.

Drug discovery today Mar 12, 2020
Predicting the binding affinity between compounds and proteins with reasonable accuracy is crucial in drug discovery. Computational prediction of binding affinity between compounds and targets greatly enhances the probability of finding lead compound...
Computational Biology Drug Development Proteins Ligands Drug Delivery Systems Drug Discovery Deep Learning Humans Machine Learning
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Combining Docking Pose Rank and Structure with Deep Learning Improves Protein-Ligand Binding Mode Prediction over a Baseline Docking Approach.

Journal of chemical information and modeling Mar 3, 2020
We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard...
Molecular Docking Simulation Deep Learning Proteins Protein Binding Ligands
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Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?

Journal of chemical information and modeling Mar 3, 2020
In recent years, protein-ligand interaction scoring functions derived through machine-learning are repeatedly reported to outperform conventional scoring functions. However, several published studies have questioned that the superior performance of m...
Algorithms Proteins Bayes Theorem Ligands Neural Networks, Computer Machine Learning
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A novel artificial intelligence protocol for finding potential inhibitors of acute myeloid leukemia.

Journal of materials chemistry. B Feb 18, 2020
There is currently no effective treatment for acute myeloid leukemia, and surgery is also ineffective as an important treatment for most tumors. Rapidly developing artificial intelligence technology can be applied to different aspects of drug develop...
Molecular Dynamics Simulation STAT3 Transcription Factor Ligands Databases, Chemical Leukemia, Myeloid, Acute Drug Discovery Protein Binding Humans Artificial Intelligence Machine Learning
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Machine learning and ligand binding predictions: A review of data, methods, and obstacles.

Biochimica et biophysica acta. General subjects Feb 10, 2020
Computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data ...
Big Data Humans Protein Binding Machine Learning Ligands Computational Biology Algorithms Drug Discovery
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Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.

Journal of chemical theory and computation Jan 16, 2020
Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have provided detailed insights into their function. Comparing the results of such simulations for a few clo...
Molecular Dynamics Simulation HIV-1 Ligands Machine Learning Humans Protein Binding Hydrogen Bonding Mutation Static Electricity Drug Resistance, Viral HIV Protease Monte Carlo Method HIV Protease Inhibitors
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Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4.

Journal of computer-aided molecular design Jan 8, 2020
Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) offered a unique opportunity for designing and testing novel methodology for accurate docking and affinity prediction of ligands in an open and blinded manner. We participated in the beta-secret...
Enzyme Inhibitors Drug Design Amyloid Precursor Protein Secretases Humans Macrocyclic Compounds Aspartic Acid Endopeptidases Protein Binding Neural Networks, Computer Binding Sites Ligands Molecular Docking Simulation
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