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augmented nlp transformer models  (Tetko Inc)

 
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    Tetko Inc augmented nlp transformer models
    Augmented Nlp Transformer Models, supplied by Tetko Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/augmented+nlp+transformer+models/augmented+nlp+transformer+models/pm40552379-446-17-8
    Average 90 stars, based on 1 article reviews
    augmented nlp transformer models - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: DirectMultiStep: Direct Route Generation for Multistep Retrosynthesis.
    Article Snippet: Model. 2020, 60, 47−55. (26) Tetko, I. V.; Karpov, P.; Van Deursen, R.; Godin, G. State-ofthe-art augmented NLP transformer models for direct and single-step retrosynthesis.

    Article Title: Investigations into the Efficiency of Computer-Aided Synthesis Planning.
    Article Snippet: International Conference on Artificial Neural Networks 2019, 11731, 817−830. (8) Tetko, I. V.; Karpov, P.; Van Deursen, R.; Godin, G. State-ofthe-art augmented NLP transformer models for direct and single-step retrosynthesis.

    Article Title: Accelerating the inference of string generation-based chemical reaction models for industrial applications.
    Article Snippet: Tetko IV, Karpov P, Van Deursen R, Godin G (2020) State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis.

    Article Title: HiRXN: Hierarchical Attention-Based Representation Learning for Chemical Reaction.
    Article Snippet: In recent years, natural language processing (NLP) techniques, including large language modeling (LLM), have contributed significantly to advancements in organic chemistry research.. Chemical reaction representations provide a link between NLP models and chemistry prediction tasks and enable the translation of complex chemical processes into a format that NLP models can understand and learn from.. However, previous representation methods fail to adequately consider the hierarchical and structural information inherent in chemical reactions.

    Article Title: Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction templates.
    Article Snippet: Tetko IV, Karpov P, Van DR et al (2020) State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis.

    Article Title: Enhancing Transthyretin Binding Affinity Prediction with a Consensus Model: Insights from the Tox24 Challenge.
    Article Snippet: 2022, 14 (1), 1. (20) Tetko, I. V.; Karpov, P.; Van Deursen, R.; Godin, G. State-ofthe-art augmented NLP transformer models for direct and single-step retrosynthesis.

    Article Title: ASKCOS: Open-Source, Data-Driven Synthesis Planning.
    Article Snippet: JACS Au 2021, 1, 1612−1620. (15) Tetko, I. V.; Karpov, P.; Van Deursen, R.; Godin, G. State-of-theart augmented NLP transformer models for direct and single-step retrosynthesis.

    Article Title: Optimizing Vanadium-Catalyzed Epoxidation Reactions: Machine-Learning-Driven Yield Predictions and Data Augmentation.
    Article Snippet: Catalytic epoxidations are key chemical processes serving as essential steps in the synthesis of commercially valuable compounds.. This study presents an innovative supervised machine learning (ML) model to predict the reaction yield of the vanadium-catalyzed epoxidation of small alcohols and alkenes.. Our framework uncovers relevant chemical characteristics for structure design, offering a pathway for automated optimization of epoxidation reactions.



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