Reliable prediction of cannabinoid receptor 2 ligand by machine learning based on combined fingerprints

Zhou, Hao; Shan, Mengyi; Qin, Lu‐Ping; Cheng, Gang

doi:10.1016/j.compbiomed.2022.106379

Cited by 8 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Eventually, the four models were assembled to build a multilayer classifier, which is freely accessible through a web platform designated Cannabinoid Iterative Revaluation for Classification and Explanation (CIRCE) that is provided with a user-friendly graphical interface. , CIRCE returns predictions on demand and instantly provides a detailed portable report of prediction outcomes. While a variety of studies have reported compound predictions for the cannabinoid receptor system, − to the best of our knowledge, CIRCE is the first free web platform enabling users to predict if a query compound might interact with CB 1 R or CB 2 R. Moreover, CIRCE’s XML framework provides model predictions and easy-to-understand color-coded maps of feature mapping to test compounds.…”

Section: Introductionmentioning

confidence: 99%

CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning

Gambacorta,

Ciriaco,

Amoroso

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

The endocannabinoid system, which includes cannabinoid receptor 1 and 2 subtypes (CB 1 R and CB 2 R, respectively), is responsible for the onset of various pathologies including neurodegeneration, cancer, neuropathic and inflammatory pain, obesity, and inflammatory bowel disease. Given the high similarity of CB 1 R and CB 2 R, generating subtype-selective ligands is still an open challenge. In this work, the Cannabinoid Iterative Revaluation for Classification and Explanation (CIRCE) compound prediction platform has been generated based on explainable machine learning to support the design of selective CB 1 R and CB 2 R ligands. Multilayer classifiers were combined with Shapley value analysis to facilitate explainable predictions. In test calculations, CIRCE predictions reached ∼80% accuracy and structural features determining ligand predictions were rationalized. CIRCE was designed as a web-based prediction platform that is made freely available as a part of our study.

show abstract

Section: Introductionmentioning

confidence: 99%

CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning

Gambacorta,

Ciriaco,

Amoroso

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…14,15 However,in recent work, deep learning-based methods have not consistently outperformed methods employing decision trees. 16,17 In molecular representation, the overall process can be regarded as two main parts: feature extraction and output prediction. Most people opt to feed the extracted features directly into a Feed-Forward Neural Network (FFN) without any additional feature engineering or selection because deep learning can automatically highlight the most important features and downplay the less relevant ones.…”

Section: Introductionmentioning

confidence: 99%

“…14,15 However,in recent work, deep learning-based methods have not consistently outperformed methods employing decision trees. 16,17 In molecular represen-tation, the overall process can be regarded as two main parts: feature extraction and output prediction.…”

Section: Introductionmentioning

confidence: 99%

ChemXTree:A Tree-enhanced Classification Approach to Small-molecule Drug Discovery

Xu,

Liu,

et al. 2023

Preprint

View full text Add to dashboard Cite

The rapid advancement of machine learning, particularly deep learning, has propelled significant strides in drug discovery, offering novel methodologies for molecular property prediction. However, despite these advancements, existing approaches often face challenges in effectively extracting and selecting relevant features from molecular data, which is crucial for accurate predictions. Our work introduces ChemXTree, a novel graph-based model that integrates tree-based algorithms to address these challenges. By incorporating a Gate Modulation Feature Unit (GMFU) for refined feature selection and a differentiable decision tree in the output layer. Extensive evaluations on benchmark datasets, including MoleculeNet and eight additional drug databases, have demonstrated ChemXTree’s superior performance, particularly in feature optimization. Permutation experiments and ablation studies further validate the effectiveness of GMFU, positioning ChemXTree as a significant advancement in molecular informatics, capable of rivaling state-of-the-art models.

show abstract

From 2015 to 2023: How Machine Learning Aids Natural Product Analysis

Shi,

Huang,

et al. 2024

Chemistry Africa

View full text Add to dashboard Cite

In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity of and volume of data generated in contemporary research endeavors. Computational methodologies represent robust tools in the field of chemistry, offering the capacity to harness potent machine learning (ML) models to yield insightful analytical outcomes. This review examines the integration of machine learning into natural product chemistry from 2015 to 2023, highlighting its potential to overcome the inherent limitations of traditional chemical techniques. We present a structured approach that matches specific natural product challenges—such as component determination, concentration prediction, and classification—with suitable ML models, including regression, classification, and dimension reduction methods. Our objective is to illustrate how ML pipelines, from data preprocessing to model evaluation, enhance both qualitative and quantitative analyses, providing a comprehensive framework, with the potential catalyze a transformation in the field of natural product analysis.

show abstract

Reliable prediction of cannabinoid receptor 2 ligand by machine learning based on combined fingerprints

Cited by 8 publications

References 42 publications

CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning

CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning

ChemXTree:A Tree-enhanced Classification Approach to Small-molecule Drug Discovery

From 2015 to 2023: How Machine Learning Aids Natural Product Analysis

Contact Info

Product

Resources

About