Direct deduction of chemical class from NMR spectra

• URL: http://arxiv.org/abs/2211.03173v1
• Date: Sun, 6 Nov 2022 16:37:47 GMT
• Title: Direct deduction of chemical class from NMR spectra
• Authors: Stefan Kuhn, Carlos Cobas, Agustin Barba, Simon Colreavy-Donnelly, Fabio Caraffini, Ricardo Moreira Borges
• Abstract summary: This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without doing structure elucidation. The method identified as suitable for the classification is a convolutional neural network (CNN)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without doing structure elucidation. This can help to reduce time in finding good structure candidates, as in most cases matching must be done by a human engineer, or at the very least a process for matching must be meaningfully interpreted by one. Therefore, for a long time automation in the area of NMR has been actively sought. The method identified as suitable for the classification is a convolutional neural network (CNN). Other methods, including clustering and image registration, have not been found suitable for the task in a comparative analysis. The result shows that deep learning can offer solutions to automation problems in cheminformatics.

Related papers

• Supervised Gradual Machine Learning for Aspect Category Detection [0.9857683394266679]
  Aspect Category Detection (ACD) aims to identify implicit and explicit aspects in a given review sentence. We propose a novel approach to tackle the ACD task by combining Deep Neural Networks (DNNs) with Gradual Machine Learning (GML) in a supervised setting.
  arXiv  Detail & Related papers  (2024-04-08T07:21:46Z)
• How neural networks learn to classify chaotic time series [77.34726150561087]
  We study the inner workings of neural networks trained to classify regular-versus-chaotic time series. We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
  arXiv  Detail & Related papers  (2023-06-04T08:53:27Z)
• From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine Reader [130.45769668885487]
  Pre-trained Machine Reader (PMR) is a novel method for retrofitting masked language models (MLMs) to pre-trained machine reading comprehension (MRC) models without acquiring labeled data. To build the proposed PMR, we constructed a large volume of general-purpose and high-quality MRC-style training data. PMR has the potential to serve as a unified model for tackling various extraction and classification tasks in the MRC formulation.
  arXiv  Detail & Related papers  (2022-12-09T10:21:56Z)
• Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming [129.4950757742912]
  We introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR) NMR aims at re-purposing a pre-trained model from a source domain to a target domain by modifying the input of a frozen pre-trained model. Experimental results suggest that a neural model pre-trained on large-scale datasets can successfully perform music genre classification by using this reprogramming method.
  arXiv  Detail & Related papers  (2022-11-02T17:38:33Z)
• Programming and Training Rate-Independent Chemical Reaction Networks [9.001036626196258]
  Natural biochemical systems are typically modeled by chemical reaction networks (CRNs) CRNs can be used as a specification language for synthetic chemical computation. We show a technique to program NC-CRNs using well-founded deep learning methods.
  arXiv  Detail & Related papers  (2021-09-20T15:31:03Z)
• Neural networks for Anatomical Therapeutic Chemical (ATC) [83.73971067918333]
  We propose combining multiple multi-label classifiers trained on distinct sets of features, including sets extracted from a Bidirectional Long Short-Term Memory Network (BiLSTM) Experiments demonstrate the power of this approach, which is shown to outperform the best methods reported in the literature.
  arXiv  Detail & Related papers  (2021-01-22T19:49:47Z)
• Margin-Based Regularization and Selective Sampling in Deep Neural Networks [7.219077740523683]
  We derive a new margin-based regularization formulation, termed multi-margin regularization (MMR) for deep neural networks (DNNs) We show improved empirical results on CIFAR10, CIFAR100 and ImageNet using state-of-the-art convolutional neural networks (CNNs) and BERT-BASE architecture for the MNLI, QQP, QNLI, MRPC, SST-2 and RTE benchmarks.
  arXiv  Detail & Related papers  (2020-09-13T15:06:42Z)
• Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach [144.21892195917758]
  We study estimation in a class of generalized Structural equation models (SEMs) We formulate the linear operator equation as a min-max game, where both players are parameterized by neural networks (NNs), and learn the parameters of these neural networks using a gradient descent. For the first time we provide a tractable estimation procedure for SEMs based on NNs with provable convergence and without the need for sample splitting.
  arXiv  Detail & Related papers  (2020-07-02T17:55:47Z)
• Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search [83.22850633478302]
  Retrosynthetic planning identifies a series of reactions that can lead to the synthesis of a target product. Existing methods either require expensive return estimation by rollout with high variance, or optimize for search speed rather than the quality. We propose Retro*, a neural-based A*-like algorithm that finds high-quality synthetic routes efficiently.
  arXiv  Detail & Related papers  (2020-06-29T05:53:33Z)
• Robust Classification of High-Dimensional Spectroscopy Data Using Deep Learning and Data Synthesis [0.5801044612920815]
  A novel application of a locally-connected neural network (NN) for the binary classification of spectroscopy data is proposed. A two-step classification process is presented as an alternative to the binary and one-class classification paradigms.
  arXiv  Detail & Related papers  (2020-03-26T11:33:52Z)
• Automated Deep Abstractions for Stochastic Chemical Reaction Networks [0.0]
  Low-level chemical reaction network (CRN) models give raise to a highly-dimensional continuous-time Markov chain (CTMC) A recently proposed abstraction method uses deep learning to replace this CTMC with a discrete-time continuous-space process. In this paper, we propose to further automatise deep abstractions for CRNs, through learning the optimal neural network architecture.
  arXiv  Detail & Related papers  (2020-01-30T13:49:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.