ArticleRSC advances2018
A machine learning approach towards the prediction of protein-ligand binding affinity based on fundamental molecular properties.
Article in RSC advances, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A unified framework combining linear and 3D molecular features for robust drug-protein interaction prediction.Cell reports methods · 2026Article
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- DOCK-PET: database of CNS kinetic parameters in the healthy human brain for existing PET tracers.Annals of nuclear medicine · 2024Article
- Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.Current drug targets · 2024Review
- Layer-Wise Relevance Analysis for Motif Recognition in the Activation Pathway of theInternational journal of molecular sciences · 2023Article
- Beyond sequence: Structure-based machine learning.Computational and structural biotechnology journal · 2023Review
- In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses.Infection and drug resistance · 2023Review
- Learning to discover medicines.International journal of data science and analytics · 2022Review
- Sfcnn: a novel scoring function based on 3D convolutional neural network for accurate and stable protein-ligand affinity prediction.BMC bioinformatics · 2022Article
- PSnpBind: a database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow.Journal of cheminformatics · 2022Article
- Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.Briefings in bioinformatics · 2022Review
- Comparison of machine learning algorithms applied to symptoms to determine infectious causes of death in children: national survey of 18,000 verbal autopsies in the Million Death Study in India.BMC public health · 2021Article
- Green chemistry and coronavirus.Sustainable chemistry and pharmacy · 2021Review
- Deep Learning in Virtual Screening: Recent Applications and Developments.International journal of molecular sciences · 2021Review
- Machine and deep learning approaches for cancer drug repurposing.Seminars in cancer biology · 2021Review
- Theobromine alleviates diet-induced obesity in mice via phosphodiesterase-4 inhibition.European journal of nutrition · 2020Article
- A Pilot Study of Multi-Input Recurrent Neural Networks for Drug-Kinase Binding Prediction.Molecules (Basel, Switzerland) · 2020Article
- Are 2D fingerprints still valuable for drug discovery?Physical chemistry chemical physics : PCCP · 2020Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is an exigency of transformation of the enormous amount of biological data available in various forms into some significant knowledge. We have tried to implement Machine Learning (ML) algorithm models on the protein-ligand binding affinity data already available to predict the binding affinity of the unknown. ML methods are appreciably faster and cheaper as compared to traditional experimental methods or computational scoring approaches. The prerequisites of this prediction are sufficient and unbiased features of training data and a prediction model which can fit the data well. In our study, we have applied Random forest and Gaussian process regression algorithms from the Weka package on protein-ligand binding affinity, which encompasses protein and ligand binding information from PdbBind database. The models are trained on the basis of selective fundamental information of both proteins and ligand, which can be effortlessly fetched from online databases or can be calculated with the availability of structure. The assessment of the models was made on the basis of correlation coefficient (
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