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Ligands

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Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques.

Experimental biology and medicine (Maywood, N.J.)
Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting pain transmission in the spinal cord. However, current opioids are addictive, often leading to overd...

DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning.

Nucleic acids research
Ribonucleic Acid (RNA) is the central conduit for information transfer in the cell. Identifying potential RNA targets in disease conditions is a challenging task, given the vast repertoire of functional non-coding RNAs in a human cell. A potential dr...

Atomic context-conditioned protein sequence design using LigandMPNN.

Nature methods
Protein sequence design in the context of small molecules, nucleotides and metals is critical to enzyme and small-molecule binder and sensor design, but current state-of-the-art deep-learning-based sequence design methods are unable to model nonprote...

Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery.

Biomolecules
Understanding protein structures can facilitate the development of therapeutic drugs. Traditionally, protein structures have been determined through experimental approaches such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy...

Leveraging Transfer Learning for Predicting Protein-Small-Molecule Interaction Predictions.

Journal of chemical information and modeling
A complex web of intermolecular interactions defines and regulates biological processes. Understanding this web has been particularly challenging because of the sheer number of actors in biological systems: ∼10 proteins in a typical human cell offer ...

Computational discovery of novel PI3KC2α inhibitors using structure-based pharmacophore modeling, machine learning and molecular dynamic simulation.

Journal of molecular graphics & modelling
PI3KC2α is a lipid kinase associated with cancer metastasis and thrombosis. In this study, we present a novel computational workflow integrating structure-based pharmacophore modeling, machine learning (ML), and molecular dynamics (MD) simulations to...

Rapid traversal of vast chemical space using machine learning-guided docking screens.

Nature computational science
The accelerating growth of make-on-demand chemical libraries provides unprecedented opportunities to identify starting points for drug discovery with virtual screening. However, these multi-billion-scale libraries are challenging to screen, even for ...

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials.

Journal of chemical information and modeling
Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations in ligand force fields continue to impact prediction accuracy. In this work, we ...

Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein-Ligand Binding Affinity Prediction.

Journal of chemical theory and computation
Directionality in molecular and biomolecular networks plays an important role in the accurate representation of the complex, dynamic, and asymmetrical nature of interactions present in protein-ligand binding, signal transduction, and biological pathw...

Can Deep Learning Blind Docking Methods be Used to Predict Allosteric Compounds?

Journal of chemical information and modeling
Allosteric compounds offer an alternative mode of inhibition to orthosteric compounds with opportunities for selectivity and noncompetition. Structure-based drug design (SBDD) of allosteric compounds introduces complications compared to their orthost...