ArticleMolecular diversity2021
Drug design by machine-trained elastic networks: predicting Ser/Thr-protein kinase inhibitors' activities.
Article in Molecular diversity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Double-head transformer neural network for molecular property prediction.Journal of cheminformatics · 2023Article
- Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning.Molecular diversity · 2022Article
- Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences.Medical & biological engineering & computing · 2021Article
- Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms.Molecular diversity · 2021Article
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
An elastic network model (ENM) represents a molecule as a matrix of pairwise atomic interactions. Rich in coded information, ENMs are hereby proposed as a novel tool for the prediction of the activity of series of molecules, with widely different chemical structures, but a common biological activity. The new approach is developed and tested using a set of 183 inhibitors of serine/threonine-protein kinase enzyme (Plk3) which is an enzyme implicated in the regulation of cell cycle and tumorigenesis. The elastic network (EN) predictive model is found to exhibit high accuracy and speed compared to descriptor-based machine-trained modeling. EN modeling appears to be a highly promising new tool for the high demands of industrial applications such as drug and material design.
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Registered trials
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