ArticleScientific reports2017
Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 71 citations in OpenAlex.
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow.Journal of cheminformatics · 2024Article
- Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review.International journal of molecular sciences · 2023Review
- Machine intelligence-driven framework for optimized hit selection in virtual screening.Journal of cheminformatics · 2022Article
- Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.Molecular diversity · 2022Review
- CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles.Journal of cheminformatics · 2021Article
- AI in drug development: a multidisciplinary perspective.Molecular diversity · 2021Article
- Targeting Cannabinoid Receptor Activation and BACE-1 Activity Counteracts TgAPP Mice Memory Impairment and Alzheimer's Disease Lymphoblast Alterations.Molecular neurobiology · 2020Article
- Influence of feature rankers in the construction of molecular activity prediction models.Journal of computer-aided molecular design · 2020Article
- Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches.Biotechnology and bioprocess engineering : BBE · 2020Review
- Machine learning-based chemical binding similarity using evolutionary relationships of target genes.Nucleic acids research · 2019Article
- Comprehensive ensemble in QSAR prediction for drug discovery.BMC bioinformatics · 2019Article
- Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library.International journal of molecular sciences · 2019Article
- QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer's Disease.Scientific reports · 2019Article
- QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease.Journal of integrative bioinformatics · 2019Article
- MoDeSuS: A Machine Learning Tool for Selection of Molecular Descriptors in QSAR Studies Applied to Molecular Informatics.BioMed research international · 2019Article
- Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges.Frontiers in pharmacology · 2018Review
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Authors and funding
11 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative structure-activity relationship modeling using machine learning techniques constitutes a complex computational problem, where the identification of the most informative molecular descriptors for predicting a specific target property plays a critical role. Two main general approaches can be used for this modeling procedure: feature selection and feature learning. In this paper, a performance comparative study of two state-of-art methods related to these two approaches is carried out. In particular, regression and classification models for three different issues are inferred using both methods under different experimental scenarios: two drug-like properties, such as blood-brain-barrier and human intestinal absorption, and enantiomeric excess, as a measurement of purity used for chiral substances. Beyond the contrastive analysis of feature selection and feature learning methods as competitive approaches, the hybridization of these strategies is also evaluated based on previous results obtained in material sciences. From the experimental results, it can be concluded that there is not a clear winner between both approaches because the performance depends on the characteristics of the compound databases used for modeling. Nevertheless, in several cases, it was observed that the accuracy of the models can be improved by combining both approaches when the molecular descriptor sets provided by feature selection and feature learning contain complementary information.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.