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Ligands

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Recent progress on the prospective application of machine learning to structure-based virtual screening.

Current opinion in chemical biology
As more bioactivity and protein structure data become available, scoring functions (SFs) using machine learning (ML) to leverage these data sets continue to gain further accuracy and broader applicability. Advances in our understanding of the optimal...

DeepFrag: An Open-Source Browser App for Deep-Learning Lead Optimization.

Journal of chemical information and modeling
Lead optimization, a critical step in early stage drug discovery, involves making chemical modifications to a small-molecule ligand to improve properties such as binding affinity. We recently developed DeepFrag, a deep-learning model capable of recom...

ET-score: Improving Protein-ligand Binding Affinity Prediction Based on Distance-weighted Interatomic Contact Features Using Extremely Randomized Trees Algorithm.

Molecular informatics
The molecular docking simulation is a key computational tool in modern drug discovery research that its predictive performance strongly depends on the employed scoring functions. Many recent studies have shown that the application of machine learning...

Ligand-Dependent Conformational Transitions in Molecular Dynamics Trajectories of GPCRs Revealed by a New Machine Learning Rare Event Detection Protocol.

Molecules (Basel, Switzerland)
Central among the tools and approaches used for ligand discovery and design are Molecular Dynamics (MD) simulations, which follow the dynamic changes in molecular structure in response to the environmental condition, interactions with other proteins,...

DeepBSP-a Machine Learning Method for Accurate Prediction of Protein-Ligand Docking Structures.

Journal of chemical information and modeling
In recent years, machine-learning-based scoring functions have significantly improved the scoring power. However, many of these methods do not perform well in distinguishing the native structure from docked decoy poses due to the lack of decoy struct...

An Integrated Approach toward NanoBRET Tracers for Analysis of GPCR Ligand Engagement.

Molecules (Basel, Switzerland)
Gaining insight into the pharmacology of ligand engagement with G-protein coupled receptors (GPCRs) under biologically relevant conditions is vital to both drug discovery and basic research. NanoLuc-based bioluminescence resonance energy transfer (Na...

Integrated support vector machine and pharmacophore based virtual screening driven identification of thiophene carboxamide scaffold containing compound as potential PARP1 inhibitor.

Journal of biomolecular structure & dynamics
Poly (ADP-ribose) polymerase-1 (PARP1) inhibition strategy for cancer treatment is gaining advantage particularly in patients having a mutation in BRCA1/BRCA2 gene. To date, four drugs have obtained FDA approval and some inhibitors are in clinical tr...

CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction.

Biomolecules
The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, w...

Deep Learning-Based Ligand Design Using Shared Latent Implicit Fingerprints from Collaborative Filtering.

Journal of chemical information and modeling
In their previous work, Srinivas et al. [ 2018, 10, 56] have shown that implicit fingerprints capture ligands and proteins in a shared latent space, typically for the purposes of virtual screening with collaborative filtering models applied on known...

Machine learning guided aptamer refinement and discovery.

Nature communications
Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, ...