Ligands

Discriminating High from Low Energy Conformers of Druglike Molecules: An Assessment of Machine Learning Potentials and Quantum Chemical Methods.

Chemphyschem : a European journal of chemical physics and physical chemistry 40017058

Accurate and efficient prediction of high energy ligand conformations is important in structure-based drug discovery for the exclusion of unrealistic structures in docking-based virtual screening and de novo design approaches. In this work, we constr...

Density Functional Theory Ligands Machine Learning Molecular Conformation Pharmaceutical Preparations Quantum Theory Thermodynamics

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Natural Language Processing Methods for the Study of Protein-Ligand Interactions.

Journal of chemical information and modeling 39993834

Natural Language Processing (NLP) has revolutionized the way computers are used to study and interact with human languages and is increasingly influential in the study of protein and ligand binding, which is critical for drug discovery and developmen...

Drug Discovery Humans Ligands Machine Learning Natural Language Processing Protein Binding Proteins

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T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment.

Journal of chemical information and modeling 39965912

There is significant interest in targeting disease-causing proteins with small molecule inhibitors to restore healthy cellular states. The ability to accurately predict the binding affinity of small molecules to a protein target in silico enables the...

Deep Learning Drug Discovery ErbB Receptors Humans Ligands Neural Networks, Computer Protein Binding Proteins SARS-CoV-2 Uncertainty

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Protein ligand structure prediction: From empirical to deep learning approaches.

Current opinion in structural biology 39914050

Protein-ligand structure prediction methods, aiming to predict the three-dimensional complex structure and binding energy of a compound and target protein, are essential in many structure-based drug discovery pipelines, including virtual screening an...

Computational Biology Deep Learning Drug Discovery Ligands Models, Molecular Protein Binding Protein Conformation Proteins

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CL-GNN: Contrastive Learning and Graph Neural Network for Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling 39913849

In the realm of drug discovery and design, the accurate prediction of protein-ligand binding affinity is of paramount importance as it underpins the functional interactions within biological systems. This study introduces a novel self-supervised lear...

Drug Discovery Graph Neural Networks Ligands Machine Learning Neural Networks, Computer Protein Binding Proteins

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Join Persistent Homology (JPH)-Based Machine Learning for Metalloprotein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling 40082211

With the crucial role of metalloproteins in respiration, oxidative stress protection, photosynthesis, and drug metabolism, the design and discovery of drugs that can target metalloproteins are extremely important. Recently, enormous potential has bee...

Ligands Machine Learning Metalloproteins Protein Binding

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Fine-Tuned Deep Transfer Learning Models for Large Screenings of Safer Drugs Targeting Class A GPCRs.

Biochemistry 40056143

G protein-coupled receptors (GPCRs) remain a focal point of research due to their critical roles in cell signaling and their prominence as drug targets. However, directly linking drug efficacy to the receptor-mediated activation of specific intracell...

Deep Learning Drug Evaluation, Preclinical Humans Ligands Receptors, G-Protein-Coupled Signal Transduction

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Development of DeepPQK and DeepQK sequence-based deep learning models to predict protein-ligand affinity and application in the directed evolution of ferulic esterase DLfae4.

International journal of biological macromolecules 40054795

Affinity plays an essential role in the rate and stability of enzyme-catalyzed reactions, thus directly impacting the catalytic activity. In general, the predictive method for protein-ligand binding affinity mainly relies on high-resolution protein c...

Amino Acid Sequence Binding Sites Carboxylic Ester Hydrolases Deep Learning Directed Molecular Evolution Ligands Models, Molecular Neural Networks, Computer Protein Binding

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iScore: A ML-Based Scoring Function for De Novo Drug Discovery.

Journal of chemical information and modeling 40036330

In the quest for accelerating de novo drug discovery, the development of efficient and accurate scoring functions represents a fundamental challenge. This study introduces iScore, a novel machine learning (ML)-based scoring function designed to predi...

Drug Discovery Ligands Machine Learning Neural Networks, Computer Protein Binding Proteins

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Increase Docking Score Screening Power by Simple Fusion With CNNscore.

Journal of computational chemistry 39981784

Scoring functions (SFs) of molecular docking is a vital component of structure-based virtual screening (SBVS). Traditional SFs yield their inherent shortage for idealized approximations and simplifications predicting the binding affinity. Complementa...

Deep Learning Drug Evaluation, Preclinical Humans Ligands Molecular Docking Simulation Neural Networks, Computer Protein Kinase Inhibitors

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Discriminating High from Low Energy Conformers of Druglike Molecules: An Assessment of Machine Learning Potentials and Quantum Chemical Methods.

Natural Language Processing Methods for the Study of Protein-Ligand Interactions.

T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment.

Protein ligand structure prediction: From empirical to deep learning approaches.

CL-GNN: Contrastive Learning and Graph Neural Network for Protein-Ligand Binding Affinity Prediction.

Join Persistent Homology (JPH)-Based Machine Learning for Metalloprotein-Ligand Binding Affinity Prediction.

Fine-Tuned Deep Transfer Learning Models for Large Screenings of Safer Drugs Targeting Class A GPCRs.

Development of DeepPQK and DeepQK sequence-based deep learning models to predict protein-ligand affinity and application in the directed evolution of ferulic esterase DLfae4.

iScore: A ML-Based Scoring Function for De Novo Drug Discovery.

Increase Docking Score Screening Power by Simple Fusion With CNNscore.

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