Direct Steering of de novo Molecular Generation using Descriptor Conditional Recurrent Neural Networks (cRNNs)

Deep learning has acquired considerable momentum over the past couple of years in the domain of de-novo drug design. Particularly, transfer and reinforcement learning have demonstrated the capability of steering the generative process towards chemical regions of interest. In this work, we propose a simple approach to the focused generative task by constructing a conditional recurrent neural network (cRNN). For this purpose, we aggregate selected molecular descriptors along with a QSAR-based bioactivity label and transform them into initial LSTM states before starting the generation of SMILES strings that are focused towards the aspired properties. We thus tackle the inverse QSAR problem directly by training on molecular descriptors, instead of iteratively optimizing around a set of candidate molecules. The trained cRNNs are able to generate molecules near multiple specified conditions, while maintaining an output that is more focused than traditional RNNs yet less focused than autoencoders. The method shows promise for applications in both scaffold hoping and ligand series generation, depending on whether the cRNN is trained on calculated scalar molecular properties or structural fingerprints. This also demonstrates that fingerprint-to-molecule decoding is feasible, leading to molecules that are similar – if not identical – to the ones the fingerprints originated from. Additionally, the cRNN is able to generate a larger fraction of predicted active compounds against the DRD2 receptor when compared to an RNN trained with the transfer learning model.

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Direct Steering of de novo Molecular Generation using Descriptor Conditional Recurrent Neural Networks (cRNNs)

10.26434/chemrxiv.9860906.v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Panagiotis-Christos Kotsias ◽

Josep Arús-Pous ◽

Hongming Chen ◽

Ola Engkvist ◽

Christian Tyrchan ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Recurrent Neural Networks ◽

Molecular Descriptors ◽

De Novo ◽

Simple Approach ◽

Regions Of Interest ◽

Generative Process ◽

The Past

Deep learning has acquired considerable momentum over the past couple of years in the domain of de-novo drug design. Particularly, transfer and reinforcement learning have demonstrated the capability of steering the generative process towards chemical regions of interest. In this work, we propose a simple approach to the focused generative task by constructing a conditional recurrent neural network (cRNN). For this purpose, we aggregate selected molecular descriptors along with a QSAR-based bioactivity label and transform them into initial LSTM states before starting the generation of SMILES strings that are focused towards the aspired properties. We thus tackle the inverse QSAR problem directly by training on molecular descriptors, instead of iteratively optimizing around a set of candidate molecules. The trained cRNNs are able to generate molecules near multiple specified conditions, while maintaining an output that is more focused than traditional RNNs yet less focused than autoencoders. The method shows promise for applications in both scaffold hoping and ligand series generation, depending on whether the cRNN is trained on calculated scalar molecular properties or structural fingerprints. This also demonstrates that fingerprint-to-molecule decoding is feasible, leading to molecules that are similar – if not identical – to the ones the fingerprints originated from. Additionally, the cRNN is able to generate a larger fraction of predicted active compounds against the DRD2 receptor when compared to an RNN trained with the transfer learning model.

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Weather Nowcasting Using Deep Learning Techniques

Data Mining - Methods, Applications and Systems ◽

10.5772/intechopen.84552 ◽

2021 ◽

Author(s):

Makhamisa Senekane ◽

Mhlambululi Mafu ◽

Molibeli Benedict Taele

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Recurrent Neural Networks ◽

Weather Forecasting ◽

Network Models ◽

Short Term ◽

Neural Network Models ◽

Precipitation Forecasting ◽

Learning Techniques

Weather variations play a significant role in peoples’ short-term, medium-term or long-term planning. Therefore, understanding of weather patterns has become very important in decision making. Short-term weather forecasting (nowcasting) involves the prediction of weather over a short period of time; typically few hours. Different techniques have been proposed for short-term weather forecasting. Traditional techniques used for nowcasting are highly parametric, and hence complex. Recently, there has been a shift towards the use of artificial intelligence techniques for weather nowcasting. These include the use of machine learning techniques such as artificial neural networks. In this chapter, we report the use of deep learning techniques for weather nowcasting. Deep learning techniques were tested on meteorological data. Three deep learning techniques, namely multilayer perceptron, Elman recurrent neural networks and Jordan recurrent neural networks, were used in this work. Multilayer perceptron models achieved 91 and 75% accuracies for sunshine forecasting and precipitation forecasting respectively, Elman recurrent neural network models achieved accuracies of 96 and 97% for sunshine and precipitation forecasting respectively, while Jordan recurrent neural network models achieved accuracies of 97 and 97% for sunshine and precipitation nowcasting respectively. The results obtained underline the utility of using deep learning for weather nowcasting.

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Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review

Revue d intelligence artificielle ◽

10.18280/ria.350101 ◽

2021 ◽

Vol 35 (1) ◽

pp. 1-10

Author(s):

Senthil Kumar Paramasivan

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Wind Energy ◽

Recurrent Neural Networks ◽

Time Series Data ◽

Short Term Memory ◽

Function Optimization ◽

Series Data ◽

Energy Forecasting

In the modern era, deep learning is a powerful technique in the field of wind energy forecasting. The deep neural network effectively handles the seasonal variation and uncertainty characteristics of wind speed by proper structural design, objective function optimization, and feature learning. The present paper focuses on the critical analysis of wind energy forecasting using deep learning based Recurrent neural networks (RNN) models. It explores RNN and its variants, such as simple RNN, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional RNN models. The recurrent neural network processes the input time series data sequentially and captures well the temporal dependencies exist in the successive input data. This review investigates the RNN models of wind energy forecasting, the data sources utilized, and the performance achieved in terms of the error measures. The overall review shows that the deep learning based RNN improves the performance of wind energy forecasting compared to the conventional techniques.

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Autonomous Tomato Harvesting Robotic System in Greenhouses: Deep Learning Classification

Mekatronika ◽

10.15282/mekatronika.v1i1.1148 ◽

2019 ◽

Vol 1 (1) ◽

pp. 80-86

Author(s):

Ooi Peng Toon ◽

Muhammad Aizzat Zakaria ◽

Ahmad Fakhri Ab. Nasir ◽

Anwar P.P. Abdul Majeed ◽

Chung Young Tan ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

South America ◽

Solanum Lycopersicum ◽

Training Data ◽

New Classification ◽

The Past ◽

Testing Data ◽

Harvesting Robot

Solanum lycopersicum or generally known as tomato came from countries of South America and has been growing in many tropical countries and its healthy nutrients in tomato becomes one of the food demand by the locals in Malaysia when their lifestyle shifted to more concern for healthy food. Since export value and production has increased for the past few years, a vast amount of labours considered for the fruit-picking process. Hence, farmers are now preferring to look for automation to replace labour problems and high cost that they are facing. To pick a correct fruit within clusters, a harvesting robot requires guidance so that it can detect a fruit accurately. In this study, a new classification algorithm using deep learning specifically convolution neural network to classify the image is either a tomato or not tomato and next, the image is classified into either a ripe or unripe tomato. Furthermore, there are two classification neural networks which are tomato or not tomato and ripe and unripe tomato. Each network consists of 600 training data and 33 testing data. The accuracies that obtained from network 1 (tomato or not tomato) and network 2 (ripe or unripe tomato) are 76.366% and 98.788% respectively.

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Perbandingan Model Deep Learning untuk Klasifikasi Sentiment Analysis dengan Teknik Natural Languange Processing

Jurnal Teknologi dan Manajemen Informatika ◽

10.26905/jtmi.v7i2.6506 ◽

2021 ◽

Vol 7 (2) ◽

pp. 113-121

Author(s):

Firman Pradana Rachman

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Sentiment Analysis ◽

Recurrent Neural Networks ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Setiap orang mempunyai pendapat atau opini terhadap suatu produk, tokoh masyarakat, atau pun sebuah kebijakan pemerintah yang tersebar di media sosial. Pengolahan data opini itu di sebut dengan sentiment analysis. Dalam pengolahan data opini yang besar tersebut tidak hanya cukup menggunakan machine learning, namun bisa juga menggunakan deep learning yang di kombinasikan dengan teknik NLP (Natural Languange Processing). Penelitian ini membandingkan beberapa model deep learning seperti CNN (Convolutional Neural Network), RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory) dan beberapa variannya untuk mengolah data sentiment analysis dari review produk amazon dan yelp.

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Fastened CROWN: Tightened Neural Network Robustness Certificates

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5944 ◽

2020 ◽

Vol 34 (04) ◽

pp. 5037-5044

Author(s):

Zhaoyang Lyu ◽

Ching-Yun Ko ◽

Zhifeng Kong ◽

Ngai Wong ◽

Dahua Lin ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Linear Programming ◽

Deep Learning ◽

Deep Neural Networks ◽

Convex Relaxation ◽

Real Life ◽

Network Robustness ◽

The Past ◽

Computationally Expensive

The rapid growth of deep learning applications in real life is accompanied by severe safety concerns. To mitigate this uneasy phenomenon, much research has been done providing reliable evaluations of the fragility level in different deep neural networks. Apart from devising adversarial attacks, quantifiers that certify safeguarded regions have also been designed in the past five years. The summarizing work in (Salman et al. 2019) unifies a family of existing verifiers under a convex relaxation framework. We draw inspiration from such work and further demonstrate the optimality of deterministic CROWN (Zhang et al. 2018) solutions in a given linear programming problem under mild constraints. Given this theoretical result, the computationally expensive linear programming based method is shown to be unnecessary. We then propose an optimization-based approach FROWN (Fastened CROWN): a general algorithm to tighten robustness certificates for neural networks. Extensive experiments on various networks trained individually verify the effectiveness of FROWN in safeguarding larger robust regions.

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Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

10.26434/chemrxiv.12562121 ◽

2020 ◽

Author(s):

Dean Sumner ◽

Jiazhen He ◽

Amol Thakkar ◽

Ola Engkvist ◽

Esben Jannik Bjerrum

Keyword(s):

Neural Networks ◽

Pattern Recognition ◽

Deep Learning ◽

Recurrent Neural Networks ◽

Data Augmentation ◽

State Of The Art ◽

Sequence Similarity ◽

Learning Models ◽

Underlying Network

SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as attentional gain – an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.

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Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

Mobile Networks and Applications ◽

10.1007/s11036-020-01703-3 ◽

2021 ◽

Vol 26 (1) ◽

pp. 200-215

Author(s):

Muhammad Alam ◽

Jian-Feng Wang ◽

Cong Guangpei ◽

LV Yunrong ◽

Yuanfang Chen

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Neural Networks ◽

Image Processing ◽

Deep Learning ◽

Semantic Segmentation ◽

Natural Scene ◽

Remote Sensing Images ◽

Advantages And Disadvantages ◽

Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

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Beyond Deep Learning: An Econometric Example

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488520400036 ◽

2020 ◽

Vol 28 (Supp01) ◽

pp. 31-38 ◽

Cited By ~ 1

Author(s):

Ruofan Liao ◽

Paravee Maneejuk ◽

Songsak Sriboonchitta

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Prediction Models ◽

Original Data ◽

Parametric Model ◽

Parametric Models ◽

The Past ◽

Currency Exchange ◽

Currency Exchange Rate ◽

Linear And Nonlinear

In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example of predicting currency exchange rate, we show that this idea indeed leads to more accurate predictions.

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