ArticleJournal of cheminformatics2016
Large-scale ligand-based predictive modelling using support vector machines.
Article in Journal of cheminformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Classifying reservoir facies using attention-based residual neural networks.PeerJ. Computer science · 2025Article
- Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.Computational and structural biotechnology journal · 2024Review
- CPSign: conformal prediction for cheminformatics modeling.Journal of cheminformatics · 2024Article
- Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review.International journal of molecular sciences · 2023Review
- PIKAChU: a Python-based informatics kit for analysing chemical units.Journal of cheminformatics · 2022Article
- Improving Compound Activity Classification via Deep Transfer and Representation Learning.ACS omega · 2022Article
- Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD).Biochemical Society transactions · 2022Article
- Article
- Machine Learning Strategies When Transitioning between Biological Assays.Journal of chemical information and modeling · 2021Article
- Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models.Journal of cheminformatics · 2021Article
- Towards reproducible computational drug discovery.Journal of cheminformatics · 2020Review
- SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines.GigaScience · 2019Article
- Computer simulations of the signalling network in FLT3BMC bioinformatics · 2018Article
- A confidence predictor for logD using conformal regression and a support-vector machine.Journal of cheminformatics · 2018Article
- Predicting Off-Target Binding Profiles With Confidence Using Conformal Prediction.Frontiers in pharmacology · 2018Article
- The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching.Journal of cheminformatics · 2017Article
- Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.Journal of cheminformatics · 2016Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.
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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.