ReviewMolecular diversity2011
Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM).
Review in Molecular diversity, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Optimization of the extraction process of Sanhuang Qingre Formula by integrating response surface methodology, grey correlation analysis, and machine learning.Scientific reports · 2026Article
- Structure-activity modeling and hybrid machine learning-based prediction of bioactivity in pyrazole derivatives for drug discovery applications.Turkish journal of biology = Turk biyoloji dergisi · 2026Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Draw on advantages and avoid disadvantages: CT-derived individualized radiomic signature for predicting chemo-radiotherapy sensitivity in unresectable advanced non-small cell lung cancer.Journal of cancer research and clinical oncology · 2024Article
- Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes.Scientific reports · 2024Article
- Structure-based molecular modeling in SAR analysis and lead optimization.Computational and structural biotechnology journal · 2021Review
- Animal simulations facilitate smart drug design through prediction of nanomaterial transport to individual tissue cells.Science advances · 2020Article
- An in vitro assay and artificial intelligence approach to determine rate constants of nanomaterial-cell interactions.Scientific reports · 2019Article
- A Method of Biomedical Information Classification Based on Particle Swarm Optimization with Inertia Weight and Mutation.Open life sciences · 2018Article
- Modeling the architecture of the regulatory system controlling methylenomycin production inJournal of biological engineering · 2017Article
- A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction.BioMed research international · 2015Article
- Multiple target drug cocktail design for attacking the core network markers of four cancers using ligand-based and structure-based virtual screening methods.BMC medical genomics · 2015Article
- Toward the computer-aided discovery of FabH inhibitors. Do predictive QSAR models ensure high quality virtual screening performance?Molecular diversity · 2014Article
- Classification of Plasmodium falciparum glucose-6-phosphate dehydrogenase inhibitors by support vector machine.Molecular diversity · 2013Article
- Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.Pharmacology & therapeutics · 2013Review
- Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines.Nucleic acids research · 2012Article
- POPISK: T-cell reactivity prediction using support vector machines and string kernels.BMC bioinformatics · 2011Article
- An alignment-free approach for eukaryotic ITS2 annotation and phylogenetic inference.PloS one · 2011Article
- Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.
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Registered trials
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.