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Tetko Inc i v transformer cnn
I V Transformer Cnn, supplied by Tetko Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/transformer-cnn/cnn+i+transformer+v/pm42045079-504-10-9
Average 86 stars, based on 1 article reviews
i v transformer cnn - by Bioz Stars, 2026-10
86/100 stars

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Article Title: Predicting lymphatic transport potential using graph transformer based on limited historical data from in vivo studies.
Article Snippet: This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record.. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article.. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Article Title: Enhancing Transthyretin Binding Affinity Prediction with a Consensus Model: Insights from the Tox24 Challenge.
Article Snippet: 2020, 11 (1), 5575. (21) Karpov, P.; Godin, G.; Tetko, I. V. Transformer-CNN: Swiss knife for QSAR modeling and interpretation.

Article Title: Generalizable, fast, and accurate DeepQSPR with fastprop.
Article Snippet: Karpov P, Godin G, Tetko IV (2020) Transformer-CNN: Swiss knife for QSAR modeling and interpretation.

Article Title: Publishing neural networks in drug discovery might compromise training data privacy.
Article Snippet: Karpov Pavel, Godin Guillaume, Tetko Igor V (2020) Swiss knife for QSAR modeling and interpretation Transformer-CNN.

Article Title: UMAP-based clustering split for rigorous evaluation of AI models for virtual screening on cancer cell lines.
Article Snippet: Karpov P, Godin G, Tetko IV (2019) Transformer‐CNN: fast and reliable tool for QSAR. arXiv Prepr arXiv191106603 42.

Article Title: PoseidonQ: A Free Machine Learning Platform for the Development, Analysis, and Validation of Efficient and Portable QSAR Models for Drug Discovery.
Article Snippet: The advent of powerful machine learning algorithms as well as the availability of high volume of pharmacological data has given new fuel to QSAR, opening new unprecedented options for deriving highly predictive models for assisting the rationale design of new bioactive compounds, for screening and prioritizing large molecular libraries, and for repurposing new drugs toward new clinical uses.. Here, we present PoseidonQ (an acronym for Personal Optimization Software for Efficient Implementation and Derivation of Online QSAR), a user-friendly software solution designed to simplify the derivation of the QSAR model for drug design and discovery.. PoseidonQ incorporates 22 machine learning algorithms, 17 types of molecular fingerprints, and 208 RDKit molecular descriptors and enables the quick derivation of both regression and classification models along with a calculated and easily interpretable applicability domain.

Article Title: Recent Advances in the Modeling of Ionic Liquids Using Artificial Neural Networks.
Article Snippet: Rep 2023, 13, 11966. (74) Karpov, P.; Godin, G.; Tetko, I. V. Transformer-CNN: Swiss Knife for QSAR Modeling and Interpretation.



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