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Ligands

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Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction.

Journal of chemical information and modeling
Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated si...

graphLambda: Fusion Graph Neural Networks for Binding Affinity Prediction.

Journal of chemical information and modeling
Predicting the binding affinity of protein-ligand complexes is crucial for computer-aided drug discovery (CADD) and the identification of potential drug candidates. The deep learning-based scoring functions have emerged as promising predictors of bin...

An ensemble-based approach to estimate confidence of predicted protein-ligand binding affinity values.

Molecular informatics
When designing a machine learning-based scoring function, we access a limited number of protein-ligand complexes with experimentally determined binding affinity values, representing only a fraction of all possible protein-ligand complexes. Consequent...

Machine learning approaches in predicting allosteric sites.

Current opinion in structural biology
Allosteric regulation is a fundamental biological mechanism that can control critical cellular processes via allosteric modulator binding to protein distal functional sites. The advantages of allosteric modulators over orthosteric ones have sparked t...

Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations.

Molecules (Basel, Switzerland)
The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sa...

Identifying potential ligand-receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis.

Computers in biology and medicine
Cell-cell communication is essential to many key biological processes. Intercellular communication is generally mediated by ligand-receptor interactions (LRIs). Thus, building a comprehensive and high-quality LRI resource can significantly improve in...

A deep learning-based theoretical protocol to identify potentially isoform-selective PI3Kα inhibitors.

Molecular diversity
Phosphoinositide 3-kinase alpha (PI3Kα) is one of the most frequently dysregulated kinases known for their pivotal role in many oncogenic diseases. While the side effects linked to existing drugs against PI3Kα-induced cancers provide an avenue for fu...

Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning.

PloS one
Predicting protein-ligand binding affinity presents a viable solution for accelerating the discovery of new lead compounds. The recent widespread application of machine learning approaches, especially graph neural networks, has brought new advancemen...

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Biomolecules
Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the va...