Generation Task Research Articles

Integrating transformers and many-objective optimization for drug design

BackgroundDrug design is a challenging and important task that requires the generation of novel and effective molecules that can bind to specific protein targets. Artificial intelligence algorithms have recently showed promising potential to expedite the drug design process. However, existing methods adopt multi-objective approaches which limits the number of objectives.ResultsIn this paper, we expand this thread of research from the many-objective perspective, by proposing a novel framework that integrates a latent Transformer-based model for molecular generation, with a drug design system that incorporates absorption, distribution, metabolism, excretion, and toxicity prediction, molecular docking, and many-objective metaheuristics. We compared the performance of two latent Transformer models (ReLSO and FragNet) on a molecular generation task and show that ReLSO outperforms FragNet in terms of reconstruction and latent space organization. We then explored six different many-objective metaheuristics based on evolutionary algorithms and particle swarm optimization on a drug design task involving potential drug candidates to human lysophosphatidic acid receptor 1, a cancer-related protein target.ConclusionWe show that multi-objective evolutionary algorithm based on dominance and decomposition performs the best in terms of finding molecules that satisfy many objectives, such as high binding affinity and low toxicity, and high drug-likeness. Our framework demonstrates the potential of combining Transformers and many-objective computational intelligence for drug design.

Integrating discourse features and response assessment for advancing empathetic dialogue

Empathetic response generation is a crucial task in natural language processing, enabling emotionally resonant machine–human interactions. In this paper, we introduce the InfRa (Integrating Discourse Features and Response Assessment) model to address limitations in traditional methods for this task, such as the lack of deep dialogue comprehension and response control. InfRa integrates discourse features to augment structural dialogue understanding, with a novel edge pruning and mutual information learning module to further refine the representation. The model also employs a response evaluation module for dynamic optimization, ensuring emotional and semantic consistency between the generated response and its context. Our experiments demonstrate that InfRa outperforms existing baselines, reducing the Perplexity (PPL) score by approximately 9 points and excelling in all three fine-grained aspects of human evaluation. This research not only advances the development of empathetic chatbots but also provides valuable insights for broader text generation tasks.

Poster Design Research Based on Deep Learning Automatic Image Generation Algorithm

Although this research has made significant progress in image generation models, they still face issues such as insufficient diversity of generated images, poor quality of high-resolution images, and the need for a large amount of training data for model optimization. This paper studies poster design based on deep learning automatic image generation algorithm, using a recursive supervised image generation algorithm framework of generative adversarial networks for multi-view image generation and super-resolution generation tasks of small sample digital poster images. Various improvements have been proposed to enhance the performance of the GAN network model for poster design image generation tasks. Based on experimental research, this paper’s model uses generative adversarial networks to distinguish randomly cropped low resolution and high-resolution poster blocks, ensuring that high-resolution posters maintain their original resolution canvas texture and brush strokes, effectively improving the automatic generation effect of poster images. The evaluation results show that the quantitative evaluation of the proposed algorithm model in knowledge management is distributed in a reasonable range, which indicates that the proposed algorithm model has good performance in knowledge management. The poster design model based on deep learning automatic image generation algorithm proposed in this paper has certain effects. In subsequent practice, the automatic image generation algorithm can be combined with practical needs to improve the efficiency and design effect of poster design.

BioInstruct: instruction tuning of large language models for biomedical natural language processing.

To enhance the performance of large language models (LLMs) in biomedical natural language processing (BioNLP) by introducing a domain-specific instruction dataset and examining its impact when combined with multi-task learning principles. We created the BioInstruct, comprising 25005 instructions to instruction-tune LLMs (LLaMA 1 and 2, 7B and 13B version). The instructions were created by prompting the GPT-4 language model with 3-seed samples randomly drawn from an 80 human curated instructions. We employed Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning. We then evaluated these instruction-tuned LLMs on several BioNLP tasks, which can be grouped into 3 major categories: question answering (QA), information extraction (IE), and text generation (GEN). We also examined whether categories (eg, QA, IE, and generation) of instructions impact model performance. Comparing with LLMs without instruction-tuned, our instruction-tuned LLMs demonstrated marked performance gains: 17.3% in QA on average accuracy metric, 5.7% in IE on average F1 metric, and 96% in Generation tasks on average GPT-4 score metric. Our 7B-parameter instruction-tuned LLaMA 1 model was competitive or even surpassed other LLMs in the biomedical domain that were also fine-tuned from LLaMA 1 with vast domain-specific data or a variety of tasks. Our results also show that the performance gain is significantly higher when instruction fine-tuning is conducted with closely related tasks. Our findings align with the observations of multi-task learning, suggesting the synergies between 2 tasks. The BioInstruct dataset serves as a valuable resource and instruction tuned LLMs lead to the best performing BioNLP applications.

Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues

Since its introduction in November 2022, ChatGPT has rapidly gained popularity due to its remarkable ability in language understanding and human-like responses. ChatGPT, based on GPT-3.5 architecture, has shown great promise for revolutionizing various research fields, including code generation. However, the reliability and quality of code generated by ChatGPT remain unexplored, raising concerns about potential risks associated with the widespread use of ChatGPT-driven code generation. In this article, we systematically study the quality of 4,066 ChatGPT-generated programs of code implemented in two popular programming languages, i.e., Java and Python, for 2,033 programming tasks. The goal of this work is threefold. First, we analyze the correctness of ChatGPT on code generation tasks and uncover the factors that influence its effectiveness, including task difficulty, programming language, time that tasks are introduced, and program size. Second, we identify and characterize potential issues with the quality of ChatGPT-generated code. Last, we provide insights into how these issues can be mitigated. Experiments highlight that out of 4,066 programs generated by ChatGPT, 2,756 programs are deemed correct, 1,082 programs provide wrong outputs, and 177 programs contain compilation or runtime errors. Additionally, we further analyze other characteristics of the generated code through static analysis tools, such as code style and maintainability, and find that 1,930 ChatGPT-generated code snippets suffer from maintainability issues. Subsequently, we investigate ChatGPT’s self-repairing ability and its interaction with static analysis tools to fix the errors uncovered in the previous step. Experiments suggest that ChatGPT can partially address these challenges, improving code quality by more than 20%, but there are still limitations and opportunities for improvement. Overall, our study provides valuable insights into the current limitations of ChatGPT and offers a roadmap for future research and development efforts to enhance the code generation capabilities of artificial intelligence models such as ChatGPT.

On the Reliability and Explainability of Language Models for Program Generation

Recent studies have adopted pre-trained language models, such as CodeT5 and CodeGPT, for automated program generation tasks like code generation, repair, and translation. Numerous language model based approaches have been proposed and evaluated on various benchmark datasets, demonstrating promising performance. However, there is still uncertainty about the reliability of these models, particularly their realistic ability to consistently transform code sequences. This raises a question: are these techniques sufficiently trustworthy for automated program generation? Consequently, further research is needed to understand model logic and assess reliability and explainability. To bridge these research gaps, we conduct a thorough empirical study of eight popular language models on five representative datasets to determine the capabilities and limitations of automated program generation approaches. We further employ advanced explainable AI approaches to highlight the tokens that significantly contribute to the code transformation. We discover that state-of-the-art approaches suffer from inappropriate performance evaluation stemming from severe data duplication, causing overoptimistic results. Our explainability analysis reveals that, in various experimental scenarios, language models can recognize code grammar and structural information, but they exhibit limited robustness to changes in input sequences. Overall, more rigorous evaluation approaches and benchmarks are critical to enhance the reliability and explainability of automated program generation moving forward. Our findings provide important guidelines for this goal.

P‐205: Exploring Potential of Language Models in OLED Materials Discovery

Language Models (LMs) have recently achieved remarkable success in natural language processing and other Artificial Intelligence (AI) applications. In this work, we adopt a language‐like representation of organic molecules and utilize LMs to address two typical tasks in the discovery of Organic Light‐Emitting Diode (OLED) materials: property prediction and structure generation. In the prediction task, the LM serves as a surrogate model of the quantum chemistry simulator for electronic properties prediction. In the generation task, the LM acts as a conditional generator for generating novel molecules with desired properties. This work demonstrates the great potential of LMs in unifying multiple tasks in OLED materials discovery within a simple but efficient framework.

Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms

In mixed-initiative co-creation tasks, wherein a human and a machine jointly create items, it is important to provide multiple relevant suggestions to the designer. Quality-diversity algorithms are commonly used for this purpose, as they can provide diverse suggestions that represent salient areas of the solution space, showcasing designs with high fitness and wide variety. Because generated suggestions drive the search process, it is important that they provide inspiration, but also stay aligned with the designer's intentions. Additionally, often many interactions with the system are required before the designer is content with a solution. In this work, we tackle these challenges with an interactive constrained MAP-Elites system that leverages emitters to learn the preferences of the designer and then use them in automated steps. By learning preferences, the generated designs remain aligned with the designer's intent, and by applying automatic steps, we generate more solutions per user interaction, giving a larger number of choices to the designer and thereby speeding up the search. We propose a general framework for preference-learning emitters (PLEs) and apply it to a procedural content generation task in the video game Space Engineers. We built an interactive application for our algorithm and performed a user study with players.

Deep Neural Networks and Tabular Data: A Survey.

Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. On homogeneous datasets, deep neural networks have repeatedly shown excellent performance and have therefore been widely adopted. However, their adaptation to tabular data for inference or data generation tasks remains highly challenging. To facilitate further progress in the field, this work provides an overview of state-of-the-art deep learning methods for tabular data. We categorize these methods into three groups: data transformations, specialized architectures, and regularization models. For each of these groups, our work offers a comprehensive overview of the main approaches. Moreover, we discuss deep learning approaches for generating tabular data and also provide an overview over strategies for explaining deep models on tabular data. Thus, our first contribution is to address the main research streams and existing methodologies in the mentioned areas while highlighting relevant challenges and open research questions. Our second contribution is to provide an empirical comparison of traditional machine learning methods with 11 deep learning approaches across five popular real-world tabular datasets of different sizes and with different learning objectives. Our results, which we have made publicly available as competitive benchmarks, indicate that algorithms based on gradient-boosted tree ensembles still mostly outperform deep learning models on supervised learning tasks, suggesting that the research progress on competitive deep learning models for tabular data is stagnating. To the best of our knowledge, this is the first in-depth overview of deep learning approaches for tabular data; as such, this work can serve as a valuable starting point to guide researchers and practitioners interested in deep learning with tabular data.

Research on Machine Learning Program Generation Algorithm Based on AORBCO

Abstract The design and development of machine learning programs require selecting appropriate data and algorithms, and coding and debugging based on specific task requirements and the programming experience of developers. However, the current knowledge structure in the field of machine learning is relatively complex, lacking systematic organization, and developers often face the problem of lack of experience when choosing algorithms and designing programs, resulting in a long development cycle and easy errors in machine learning programs. In response to the above issues, this article proposes and designs a machine learning program generation algorithm based on the AORBCO model. The program generation ability includes two sub abilities: algorithm decision-making ability and code generation ability. AD-EKG has been designed for algorithmic decision-making ability, allowing Ego to select appropriate machine learning algorithms based on datasets in massive data. This algorithm combines the characteristics of the AORBCO model's domain knowledge base, knowledge graph based recommendation algorithm, and collaborative filtering algorithm. By calculating the descriptive and structural information between the dataset and algorithm, the interaction probability between the dataset and algorithm is obtained, allowing Ego to make algorithmic decisions interaction probability based. Results of the experiment have shown that the AD-EKG algorithm can fully utilize structural and descriptive information to improve the accuracy of Ego algorithm decision-making. CodeT5-EKG has been designed for code generation capability, allowing Ego to automatically generate machine learning program code. This algorithm combines the CodeT5 generative model with the domain knowledge base of the AORBCO model, by adding auxiliary information extracted using DPR technology to the code generation task, and performing diversified fusion operations to improve code generation quality. The CodeT5-EKG algorithm combines the creativity and efficiency of generative models and DPR technology, and is an algorithm that can improve the quality of generated code while also having the advantages of generative models. The experiments have proved that the code generated by this algorithm has better quality compared to other generative models with the same number of parameters.

Gemini MultiPDF Chatbot: Multiple Document RAG Chatbot using Gemini Large Language Model

Abstract: The Gemini MultiPDF Chatbot represents a groundbreaking advancement in natural language processing (NLP) by integrating Retrieval-Augmented Generation (RAG) techniques with the Gemini Large Language Model. This innovative chatbot is designed to handle multiple document retrieval and generation tasks, leveraging the extensive knowledge base of the Gemini model. By harnessing RAG methods, the chatbot enhances its ability to acquire, comprehend, and generate responses across diverse knowledge sources contained within multiple PDF documents. The integration of Gemini's powerful language understanding capabilities with RAG facilitates seamless interaction with users, offering comprehensive and contextually relevant responses. This paper presents the design, implementation, and evaluation of the Gemini MultiPDF Chatbot, demonstrating its effectiveness in navigating complex information landscapes and delivering high-quality conversational experiences.

A YOLO-based deep learning model for Real-Time face mask detection via drone surveillance in public spaces

Automating face mask detection in public areas is paramount to maintaining public health, especially in the context of the COVID-19 pandemic. Utilization of technologies such as deep learning and computer vision systems enables effective monitoring of mask compliance, thereby minimizing the risk of virus spread. Real-time detection helps in prompt intervention for and enforcement of the use of masks, thereby preventing potential outbreaks and ensuring compliance with public health guidelines. This method helps save human resources and makes the reinforcement of wearing masks in public areas consistent and objective. Automatic detection of face masks serves as a key tool for preventing the spread of contagious diseases, protecting public health, and creating a safer environment for every person. This study addresses the challenges of real-time face mask detection via drone surveillance in public spaces, with reference to three categories: wearing of mask, incorrect wearing of mask, and no mask. Addressing these challenges entails an efficient and robust object detection and recognition algorithm. This algorithm can deal with a crowd of multiple faces via a mobile camera carried by a mini drone, and performs real-time video processing. Accordingly, this study proposes a You Only Look Once (YOLO) based deep learning C-Mask model for real-time face mask detection and recognition via drone surveillance in public spaces. The C-Mask model aims to operate within a mini drone surveillance system and provide efficient and robust face mask detection. The C-Mask model performs preprocessing, feature extraction, feature generation, feature enhancement, feature selection, and multivariate classification tasks for each face mask detection cycle. The preprocessing task prepares the training and testing data in the form of images for further processing. The feature extraction task is performed using a Convolutional Neural Network (CNN). Moreover, Cross-Stage Partial (CSP) DarkNet53 is used to improve the feature extraction and to facilitate the model’s object detection ability. A data augmentation algorithm is used for feature generation to enhance the model’s training robustness. The feature enhancement task is performed by applying the Path Aggregation Network (PANet) and Spatial Pyramid Pooling Network (SPPNet) algorithms, which are deployed to enhance the extracted and generated features. The classification task is performed through multi-label classification, wherein each object in an image can belong to multiple classes simultaneously, and the network generates a grid of bounding boxes and corresponding confidence scores for each class. The YOLO-based C-Mask model testing is performed by experimenting with various face mask detection scenarios and with varying mask colors and types, to ensure the efficiency and robustness of the proposed model. The C-Mask model test results show that this model can correctly and effectively detect face masks in real-time video streams under various conditions with an overall accuracy of 92.20%, precision of 92.04, recall of 90.83%, and F1-score of 89.95%, for all the three classes. These high scores have been obtained despite mini drone mobility and camera orientation adjustment substantially affecting face mask detection performance.

Benchmarking Llama 3 70B for Code Generation: A Comprehensive Evaluation

This study benchmarks the capabilities of Llama 3 70B, a 70-billion parameter large language model (LLM), for code generation tasks. To effectively train and fine-tune this massive model, we integrate PyTorch Fully Sharded Data Parallel (FSDP) [1], [2] for distributed training and Quantized Low-Rank Adaptation (Q-LoRA) [7] for efficient fine-tuning. We address challenges associated with distributed training, including communication overhead and synchronization complexities, through optimization strategies like gradient accumulation, optimizer state sharding, and mixed precision training. Additionally, we employ advanced training techniques such as Curriculum Learning, Dynamic Batch Sizing, and Adaptive Optimization Algorithms to enhance model performance and training efficiency. Our primary focus is evaluating the performance of the fine-tuned Llama 3 70B model on two widely-recognized code generation benchmarks: HumanEval [8] and MBPP [9]. HumanEval assesses the model's ability to translate natural language problem descriptions into functionally correct code, while MBPP evaluates its proficiency in solving complex programming problems by generating accurate Python code. We present detailed performance results on these benchmarks, analyzing the model's strengths and limitations in various code generation scenarios. Furthermore, we compare the impact of our training and fine-tuning methodologies on scalability, memory efficiency, and training speed, demonstrating the feasibility and efficiency of our approach. This benchmark study offers valuable insights for researchers and practitioners exploring the application of LLMs for code generation. It provides a comprehensive evaluation of Llama 3 70B's capabilities, sheds light on the effectiveness of various training and fine-tuning techniques, and emphasizes the importance of rigorous benchmark evaluation in driving progress within this rapidly evolving field.

A deep learning and large group consensus based cruise satisfaction evaluation model with online reviews

Cruise satisfaction assessment is pivotal for both passenger experiences and the enterprise's industry competitiveness. To do that, this research proposes a deep learning based cruise satisfaction evaluation model by integrating cruise online reviews and a large group consensus model. In order to acquire the dimensions influencing cruise passenger satisfaction, attribute extraction is employed. Unlike traditional methods that rely on word-based attribute representation, this method employs sentence-level information to comprehensively capture attributes that are of genuine concern to users, including implicit ones. Additionally, it utilizes the pre-trained language model, BART, to accurately predict sentiment polarity by treating sentiment analysis as a generation task based on online reviews from the CruiseCritic website. Subsequently, the proposed consensus model is implemented to determine the final satisfaction level, taking into consideration subgroup weights and an ideal solution-based feedback mechanism. This mechanism facilitates the provision of reasonable adjustment recommendations tailored to the context of online evaluations. Ultimately, the research findings contribute valuable insights to enhance service quality within the Royal Caribbean International cruise line.

A single-image GAN model using self-attention mechanism and DenseNets

Image generation from a single natural image using generative adversarial networks (GANs) has attracted extensive attention recently due to the GANs’ practical ability to produce photo-realistic images and their potential applications in computer vision. However, learning a powerful generative model that generates realistic, high-quality images from only a single natural image is still a challenging problem. Training GANs in limited data regimes often causes some issues, such as overfitting, memorization, training divergence, poor image quality, and a long training time. In this study, we investigated the state-of-the-art GAN models in computer vision tasks. We conducted several experiments to deeply understand the challenges of learning a powerful generative model. We introduced a novel unconditional GAN model that produces realistic, high-quality, diverse images based on a single training image. In our model, we employed a self-attention mechanism (SAM), a densely connected convolutional network (DenseNet) architecture, and a relativistic average least-squares GAN with gradient penalty (RaLSGAN-GP) for both the generator and discriminator networks to perform image generation tasks better. SAM controls the global contextual information level. It is complementary to convolutions for large feature maps and gives the generator and discriminator more capability to capture long-range dependencies in feature maps. It compensates for the long training time and low image quality issues. DenseNet connects each layer to every other layer in a feed-forward manner to ensure maximum information flow between layers in the network. It is highly parameter efficient and requires less computation to achieve high performance. It provides improved information and gradient flow throughout the network for easy training. It has a regularizing effect that reduces overfitting in image generation. RaLSGAN-GP further improves data generation quality and the stability of our model at no computational cost and provides much more stable training. Thanks to the appropriate combination of SAM, DenseNet, and RaLSGAN-GP, our model successfully generates realistic, high-quality, diverse images while maintaining the global context of the training image. We conducted experiments, user studies, and model evaluation methods to test our model’s performance and compared it with the previous well-known models on three datasets (Places, LSUN, ImageNet). We demonstrated our model’s capability in image synthesis and image manipulation tasks. In our experiments, we showed that our model utilized parameters more efficiently, prevented overfitting, better captured the internal patch statistics of images with complex structures and textures, achieved comparable performance in single image generation tasks, and had much better visual results than its competitive peers. User studies confirmed that the images generated by our model were commonly confused with the original images. Our model can provide a powerful tool for various image manipulation tasks as well as data augmentation in domains dealing with limited training data.

A new physical reservoir using the complex dynamics of electric fields in type-II superconductors containing pinning centers interacting with quantized magnetic flux lines

Reservoir computing, which takes advantage of physical phenomena with nonlinearities, is attracting a lot of attention. Therefore, it is expected to focus on superconductivity, a physical phenomenon with nonlinearities between the output electric field and the input current density. Compared to other physical phenomena used in reservoirs, it is considered that with superconductivity, the nonlinearity can easily be adjusted spatially by pin placement to produce dynamics suitable for reservoirs. The nonlinearity between the electric field generated by the motion of the quantized magnetic flux lines and the input current density was used to perform a reservoir computing task. Three waveform generation tasks, a NARMA2 task and a nonlinear-memory task were performed, and all tasks were generally successful. It was found that the electromagnetic phenomenon in superconductors can be used as a physical reservoir.

StereoHate: Toward identifying stereotypical bias and target group in hate speech detection

Abstract Though social media helps spread knowledge more effectively, it also stimulates the propagation of online abuse and harassment, including hate speech. It is crucial to prevent hate speech since it may have serious adverse effects on both society and individuals. Therefore, it is not only important for models to detect these speeches but to also output explanations of why a given text is toxic. While plenty of research is going on to detect online hate speech in English, there is very little research on low-resource languages like Hindi and the explainability aspect of hate speech. Recent laws like the “right to explanations” of the General Data Protection Regulation have spurred research in developing interpretable models rather than only focusing on performance. Motivated by this, we create the first interpretable benchmark hate speech corpus hate speech explanation (HHES) in the Hindi language, where each hate post has its stereotypical bias and target group category. Providing descriptions of internal stereotypical bias as an explanation of hate posts makes a hate speech detection model more trustworthy. Current work proposes a commonsense-aware unified generative framework, CGenEx, by reframing the multitask problem as a text-to-text generation task. The novelty of this framework is it can solve two different categories of tasks (generation and classification) simultaneously. We establish the efficacy of our proposed model (CGenEx-fuse) on various evaluation metrics over other baselines when applied to the Hindi HHES dataset. Disclaimer The article contains profanity, an inevitable situation for the nature of the work involved. These in no way reflect the opinion of authors.

Towards accurate unsupervised video captioning with implicit visual feature injection and explicit

In the realm of the video captioning field, acquiring large amounts of high-quality aligned video–text pairs remains laborious, impeding its practical applications. Therefore, we explore the modelling techniques for unsupervised video captioning. Using text inputs similar to the video representation to generate captions has been a successful unsupervised video captioning generation strategy in the past. However, this setting relies solely on the textual data for training, neglecting vital visual cues related to the spatio-temporal appearance within the video. The absence of visual information increases the risk of generating erroneous video captions. In view of this, we propose a novel unsupervised video captioning method that introduces visual information related to text features keywords to implicitly enhance training for text generation tasks. Simultaneously, our method incorporates sentence to explicitly augment the training process. our method injects additional implicit visual features and explicit keywords into the model, Which can inject the generated captions with more accurate semantics. the experimental analysis demonstrates the merit of the proposed formulation, achieving superior performance against the state-of-the-art unsupervised studies.

Room Interior Generation Using Stable Diffusion Models

The present paper puts forward an innovative method for the fine tuning of Stable Diffusion with the use of Dreambooth, which is a very fast technique in the image generation task. In particular, our approach covers the topic of room interior generation. It offers a quick option to instruct the algorithm with the most recent concepts, without the need to retrain it. Using a shortened list of instance and class prompts as examples we introduce a revolutionary training pipeline where class and instance information interacts to order a model's learning process. By conducting elaborate trials where we demonstrate that our methodology out-perform the competitors in the realistic visualization, which is based on the given theme. We use an extensive evaluation process to prove the effectiveness of the method on many datasets, which can ensure its generalization capacity to unseen various layouts of the rooms and their interior design. On one hand, precise ablation studies are carried out in order to evaluate the influence of the distinguished components in the given model. This paper shows the encompassing work of Dreambooth as a tool of choice for the personalized room interior synthesis. The new possibilities to fine-tune the generative models are also becoming a subject for futurological research in the interior design area.

Enhancing MD simulations: ASGARD's automated analysis for GROMACS.

Molecular Dynamics (MD) simulations are essential in analyzing the physical movement of molecules, with GROMACS being a widely recognized open-source package for this purpose. However, conducting analyses individually in GROMACS can take excessive time and effort. Addressing this challenge, we introduce ASGARD, an innovative workflow designed to streamline and automate the analysis of MD simulation of protein or protein-ligand complex. Unlike the traditional, manual approach, ASGARD enables researchers to generate comprehensive analyses with a single command line, significantly accelerating the research process and avoiding the laborious task of manual report generation. This tool automatically performs a range of analyses post-simulation, including system stability and flexibility assessments through RMSD Fluctuation and Distribution calculations. It further provides dynamic analysis using SASA, DSSP method graphs, and various interaction analyses. A key feature of ASGARD is its user-friendly design; it requires no additional installations or dependencies, making it highly accessible for researchers. In conclusion, ASGARD simplifies the MD simulation analysis process and substantially enhances efficiency and productivity in molecular research by providing an integrated, one-command analysis solution.Communicated by Ramaswamy H. Sarma.