Deep Learning for Drug Discovery: A Study of Identifying High Efficacy Drug Compounds Using a Cascade Transfer Learning Approach

Zhuang, Dylan; Ibrahim, Ali K.

doi:10.3390/app11177772

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…The experiments were implemented in Matlab. First, a standard validation procedure was followed [26,27]. Next, the experimental dataset from PhysioNet was divided into 80% and 20%, where 80% of the data was used for training, and 20% of the data was reserved for testing.…”

Section: Model Testing Procedures and Evaluation Metricsmentioning

confidence: 99%

A Machine Learning Approach to Automatic Classification of Eight Sleep Disorders

Zhuang¹,

Rao²,

Ibrahim³

2022

Preprint

Self Cite

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This research focuses on automatically classifying common sleep disorders. We attempt to answer the following basic research questions: Is a machine learning model able to classify all types of sleep disorders with high accuracy? Among the different modalities of sleep disorder signals, are some more important than others? Do raw signals improve the performance of a deep learning model when they are used as inputs? Prior research showed that most sleep disorders belong to eight categories (for instance, the PhysioBank dataset). To study the performance of machine learning models in classifying polysomnography recordings into the eight categories of sleep pathologies, we selected the Cyclic Alternating Pattern (CAP) Sleep Database, a collection of 108 polysomnographic recordings. We developed a multi-channel Deep Learning (DL) model where a set of Convolutional Neural Networks (CNNs) were applied to six channels of raw signals of different modalities, including three channels of EEG (lectroencephalogram) signals and one channel each of EMG (electromyography), ECG (electrocardiogram), and EOG (electrooculogram) signals. To compare the performance of the DL model with other models, we designed a model that took spectral features, instead of raw signals, as its inputs. We named the former the DL-R model and the latter the DL-F model. For comparison, we also designed two conventional classifiers, Random Forest (RF) and Support Vector Machines (SVM), which also took spectral features as inputs. We first studied the "importance" issue of signal modalities using the RF algorithm. We found that ECG contributed most to the important features and EMG second, among the four signal modalities. We then studied the accuracy performance of the proposed machine learning models. We verified that the multi-channel DL-R model, which took raw signals as its inputs, outperformed all other models, with its sensitivity and specificity scores both being above 95% across all the eight sleep disorders. This accuracy performance is on a par with those published results which dealt with fewer types of sleep disorders. To study the explanability of the DL-R model, we adopted two popular heatmap-generating techniques, with which we confirmed that the DL model's superior performance was owing to the CNN network's ability to extract potent features from raw signals. Its heatmaps produced frequency ranges and peak frequencies of various sleep disorders consistent with the results from clinic studies. We hope that the proposed approach is one step closer to more able and trustworthy machine learning techniques that one day will be adopted by practitioners.

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Section: Model Testing Procedures and Evaluation Metricsmentioning

confidence: 99%

A Machine Learning Approach to Automatic Classification of Eight Sleep Disorders

Zhuang¹,

Rao²,

Ibrahim³

2022

Preprint

Self Cite

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“…The initial learning rate is set to 10 −4 . All models were trained to predict 10, 20, 30 frames given 10 input frames, where we use the teacher forcing mechanism (Su et al, 2020;Zhuang and Ibrahim, 2021), when a step is within the number of input steps, it's direct multi-step forecast strategy, when the step exceeds the number of input steps, it adopts recursive multi-step forecast strategy. These baseline models are listed below: 2020) is a higher-order generalization to ConvLSTM, which is able to learn long-term spatio-temporal structure in videos.…”

Section: Model Implementationmentioning

confidence: 99%

Physics-Informed Tensor-Train ConvLSTM for Volumetric Velocity Forecasting of the Loop Current

Huang

Tang

Zhuang

et al. 2021

Front. Artif. Intell.

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According to the National Academies, a week long forecast of velocity, vertical structure, and duration of the Loop Current (LC) and its eddies at a given location is a critical step toward understanding their effects on the gulf ecosystems as well as toward anticipating and mitigating the outcomes of anthropogenic and natural disasters in the Gulf of Mexico (GoM). However, creating such a forecast has remained a challenging problem since LC behavior is dominated by dynamic processes across multiple time and spatial scales not resolved at once by conventional numerical models. In this paper, building on the foundation of spatiotemporal predictive learning in video prediction, we develop a physics informed deep learning based prediction model called—Physics-informed Tensor-train ConvLSTM (PITT-ConvLSTM)—for forecasting 3D geo-spatiotemporal sequences. Specifically, we propose (1) a novel 4D higher-order recurrent neural network with empirical orthogonal function analysis to capture the hidden uncorrelated patterns of each hierarchy, (2) a convolutional tensor-train decomposition to capture higher-order space-time correlations, and (3) a mechanism that incorporates prior physics from domain experts by informing the learning in latent space. The advantage of our proposed approach is clear: constrained by the law of physics, the prediction model simultaneously learns good representations for frame dependencies (both short-term and long-term high-level dependency) and inter-hierarchical relations within each time frame. Experiments on geo-spatiotemporal data collected from the GoM demonstrate that the PITT-ConvLSTM model can successfully forecast the volumetric velocity of the LC and its eddies for a period greater than 1 week.

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“…However, AI techniques such as machine learning (ML) and natural language processing offer the potential to accelerate and improve this process by enabling more efficient and accurate analysis of large amounts of data [ 4 ]. The successful use of deep learning (DL) to predict the efficacy of drug compounds with high accuracy has been described recently by the authors of [ 5 ]. AI-based methods have also been able to predict the toxicity of drug candidates [ 6 ].…”

Section: Introduction To Ai and Its Potential For Use In Drug Discoverymentioning

confidence: 99%

The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies

et al. 2023

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Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges, and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research, are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, in terms of assisting human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, the human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and the scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.

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Deep Learning for Drug Discovery: A Study of Identifying High Efficacy Drug Compounds Using a Cascade Transfer Learning Approach

Cited by 11 publications

References 22 publications

A Machine Learning Approach to Automatic Classification of Eight Sleep Disorders

A Machine Learning Approach to Automatic Classification of Eight Sleep Disorders

Physics-Informed Tensor-Train ConvLSTM for Volumetric Velocity Forecasting of the Loop Current

The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies

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