Intelligent Program Research Articles

Structural Modeling of Nanobodies: A Benchmark of State-of-the-Art Artificial Intelligence Programs.

The number of applications for nanobodies is steadily expanding, positioning these molecules as fast-growing biologic products in the biotechnology market. Several of their applications require protein engineering, which in turn would greatly benefit from having a reliable structural model of the nanobody of interest. However, as with antibodies, the structural modeling of nanobodies is still a challenge. With the rise of artificial intelligence (AI), several methods have been developed in recent years that attempt to solve the problem of protein modeling. In this study, we have compared the performance in nanobody modeling of several state-of-the-art AI-based programs, either designed for general protein modeling, such as AlphaFold2, OmegaFold, ESMFold, and Yang-Server, or specifically designed for antibody modeling, such as IgFold, and Nanonet. While all these programs performed rather well in constructing the nanobody framework and CDRs 1 and 2, modeling CDR3 still represents a big challenge. Interestingly, tailoring an AI method for antibody modeling does not necessarily translate into better results for nanobodies.

Knowledge-Based Features for Speech Analysis and Classification: Pronunciation Diagnoses

Accurate pronunciation of speech sounds is essential in communication. As children learn their native language, they refine the movements necessary for intelligible speech. While there is variability in the order of acquisition of speech sounds, there are some sounds that are more complex and are later developing. The rhotic /r/ is a later-developing sound in English, and some children require intervention to achieve accurate production. Additionally, individuals learning English as a second language may have difficulty learning accurate /r/ production, especially if their native language does not have an /r/, or the /r/ they produce is at a different place of articulation. The goal of this research is to provide a novel approach on how a knowledge-based intelligence program can provide immediate feedback on the accuracy of productions. In the proposed approach, the audio signals will first be detected, after which features of audio signals will be extracted, and finally, knowledge-based intelligent classification will be performed. Based on the obtained knowledge and application scenarios, novel features are proposed and used to classify various speaker scenarios.

Research on dynamic path planning method of electric inspection robot based on fuzzy neural network

In order to better address the issue of the coverage rate about power inspection robot to the working area, solving the difficulties in the navigation modeling and path planning of the intelligent control program is the focus of this paper. This paper first analyzes a multi-information fusion SLAM robot positioning and navigation algorithm, then uses the Kalman filter technology to locate through Voronoi mapping. Second, this paper proposes a five-layer neural network approach to how robots can plan and control routes in high-density scenarios. Third, the robot takes the surrounding obstacle distance, the target angle and the target distance as input parameters then the output parameters of the fuzzy neural network are the wheel speed and the steering angle. The inspection robot uses the sensors on the body to detect the surrounding environment’s parameter information. The dynamic path planning algorithm changes the path accordingly, prevents collision, and finally reaches the preset target. Finally, the paper collects the features obtained by Voronoi mapping in a large-scale simulation environment. The fuzzy neural network algorithm for detecting robots is validated in a complex environment, proving the effectiveness and accuracy of the algorithm.

Intelligent adaptive control method for key parameters of vibration rolling during high-speed railway subgrade compaction

To improve the intelligent construction of high-speed railway foundations using continuous subgrade compaction control techniques, a large amount of measured data was accumulated through rolling tests at four sites along the Tianjin Beichen Station high-speed railway foundation. An intelligent adaptive control program for key technical parameters of vibration rolling was then proposed using a long short-term memory neural network. The accuracy of the proposed method was verified, the corresponding calculation process presented, and case validation carried out. The results show that between the measured and predicted values of the compaction quality control index, the average absolute value percentage error (APE) was limited to within 10% with a minimum of 4%, and the mean APE was limited to within 5.5% with a minimum of 3.397%; and the root mean square error was limited to within 2.5 with a minimum of 1.135. These results demonstrate that the proposed model was successfully trained to store the non-linear function describing the relationship between the data in the system and that its learning effect was reasonable, meeting the accuracy requirements of the project. The proposed method can fulfil the requirements for real-time precise intelligent compaction by determining the key parameters of the vibratory roller. It can thereby realize modular management and precise pressure compensation to ensure the efficiency and quality of high-speed railway subgrade fill compaction by vibratory rolling.

An Intelligent Path Planning of Welding Robot Based on Multisensor Interaction

Industrial robots are evolving rapidly in the manufacturing industry. There are two main techniques for programming the robots such as online and offline programming. However, the time spent on programming a new trajectory is a major challenge in deploying welding robots which makes these approaches less efficient. This paper presents a two-stage method employing multi-sensor interaction for the path planning of a welding robot. The proposed scheme enhances weld seam trajectory development and creates a highly adjustable intelligent guidance programming system for welding robots. A global stage approach utilizing the RGB-D camera which combines fast 2D object recognition and 3D reconstruction models is proposed to quickly identify the coarse trajectory. The processes of fast 2D object recognition and 3D reconstruction are carried out using a deep neural network model and stereo vision sensor module. This technique can be a better replacement for offline programming or hand gesture controls also known as the teaching trajectory, particularly for the welding robot application. The local positioning stage is then applied using the laser vision module to obtain more precise information of the local environment, guided by the coarse trajectory that was realized in the prior stage (global stage). The efficacy of the proposed system is analyzed by conducting numerous tests using an experimental setup. The experimental finding demonstrates that the suggested study has great potential to automate the welding robot in the manufacturing sector.

Digital Transformation to achieve Sustainability (UAE development plan till 2030- case Study)

Problem of the Research- lately the economic environment is completely differing than 20th century and even than the last decades of 21st century. Novel idioms, notable expectations have been innovated that related to economic, social, political and even behavioral aspects of life; the competency does not limit with qualified intellectual human resources rather than the use of knowledge and novel aspects of technologies due to the rapid dynamic development of virtual economy. Thus, global economies experienced a rebirth of economic system. Design, Methodology, Approach- it is an inductive study “UAE” as a sample; in which it extrapolates and analyze the studies and essays that related to economic digitization. Descriptive and analytical tools used to study the related aspects of phenomena. Finding- UAE in general and Dubai in specific has a great role in achieving many entrepreneurial projects as a part of intelligent governmental program. Digitization programs become an obvious feature in most governmental programs such as medical, smart educational and banking usages; meanwhile, these programs are available for all in a lowest price of cost. Dubai tries to follow its vision for (2020-2030) of achieving sustainability through rapid change in technology towards green-zone technology environment under the supervision of rational policy of decisions, then expands it experiences to UAE as a whole. Research limitations- implications- the study covers only studies and essays that related to scan UAE “Dubai” entrepreneurial projects for the last decade of 20th century and early decades of 21st century. Practical implications- the study confines to use the descriptive, analytical tools to study the related phenomena. Social implications- it specifics to review the Dubai Emirate as a society of study. Objectives of the Research Transferring the societies into digital ones to expand the career opportunities especially among adult talents. Distinguishing the economic digitalization & digital economy. Analyzing the main threats toward digital economy. Focusing on the role of digital economy especially for those graduated talents. Importance of the Research- as far as the study reviews, few studies in Arabian libraries are handling this dynamic topic; and still the meaning of facilitating the digitization to achieve sustainability is not clear enough, meanwhile, UAE considered as a “Spark” to most of Arabian countries. Thus, having their successful experience of entrepreneurial projects would be a “guide” for followers.

Twitter Bot Detection Using Diverse Content Features and Applying Machine Learning Algorithms

A social bot is an intelligent computer program that acts like a human and carries out various activities in a social network. A Twitter bot is one of the most common forms of social bots. The detection of Twitter bots has become imperative to draw lines between real and unreal Twitter users. In this research study, the main aim is to detect Twitter bots based on diverse content-specific feature sets and explore the use of state-of-the-art machine learning classifiers. The real-world data from Twitter is scrapped using Twitter API and is pre-processed using standard procedure. To analyze the content of tweets, several feature sets are proposed, such as message-based, part-of-speech, special characters, and sentiment-based feature sets. Min-max normalization is considered for data normalization and then feature selection methods are applied to rank the top features within each feature set. For empirical analysis, robust machine learning algorithms such as deep learning (DL), multilayer perceptron (MLP), random forest (RF), naïve Bayes (NB), and rule-based classification (RBC) are applied. The performance evaluation based on standard metrics of precision, accuracy, recall, and f-measure reveals that the proposed approach outperforms the existing studies in the relevant literature. In addition, we explore the effectiveness of each feature set for the detection of Twitter bots.

Virtual Assistance using Python

The paper explores the potential of utilizing new technology to develop an intelligent Virtual Assistant that is capable of using natural language processing and user-based data. It examines existing intelligent programs with different categories of support and evaluates the potential usefulness of a particular software as a Virtual Assistant. The proposed Virtual Assistant should be able to communicate socially through natural language processing, store and analyze user data, and operate without the need for human input or programming. The paper suggests that with advancements in technology, creating virtual personal assistants could become a reality. The authors conducted experiments on a specific software and performed user testing, which demonstrated that a basic program with natural language processing algorithms could already be viable. Overall, the paper presents the idea of an intelligent Virtual Assistant that could revolutionize the way we interact with technology.

Взаимосвязь человека и робота как феномен социального взаимодействия

<p style="text-align: justify;"><strong>Objective</strong><strong>.</strong> Development of general theoretical and methodological concepts for the analysis of the interaction "human-robot" as a holistic human-technological gestalt; introduction into scientific circulation of the concept of human-technological reality; the formulation of the author's definition of personal space as a methodological basis for the study of human-technological relations.<br><strong>Background.</strong> The creation of high-tech computer cognitive agents and humanoid robots are becoming a priority subject of scientific research in various fields of knowledge. The main difficulty in moving towards any single generally valid results lies in the fact that in each discipline the problem is investigated within the framework of its inherent terminology. The multivariance of interpretations of studies of human-social robot interaction is especially observed between representatives of the humanities, specialists in the field of artificial intelligence and engineering programming. There is a need for a systematic elaboration of the conceptual and categorical apparatus used to describe the phenomena of human-technological reality, in which a person coexists with new technological beings, is called upon to establish a certain level of relations with them, redistribute personal space, and form new models of social interaction.<br><strong>Methodology.</strong> Convergent approach, system analysis, principle of integrity, typology of relations "I-Thou" by S.L. Frank.<br><strong>Conclusions.</strong> Consideration of the typology of I-Thou relations, the author's definition of personal space, the introduction into scientific circulation of the concept of human-technological reality, highlighting the features of human-technological relations are proposed for use as a conceptual and methodological toolkit for researching human-robot interaction as a phenomenon of social interaction.</p>

Application of Artificial Intelligence in the Assessment and Forecast of Avalanche Danger in the Ile Alatau Ridge

The assessment and forecast of avalanche danger are very important means of preventing avalanche fatalities, especially in recreational areas. The use of artificial intelligence methods for these purposes significantly increases the accuracy of avalanche forecasts. The purpose of this re-search was to improve the methods for assessing and forecasting avalanche danger in the Ile Alatau Ridge. To create a training sample, the data from three meteorological and two avalanche stations for the period from 2002 to 2022 were used. The following predictors were chosen: air temperature, snow cover depth, precipitation, and snowpack stability index. The subject of the assessment and forecasts was the level of avalanche danger, assessed on a five-point scale. The program Statistica StatSoft was used as a neurosimulator. When forecasting avalanche danger, the predictive values of air temperature and precipitation, obtained from numerical weather forecast models, were used. The model correctly assessed the current level of avalanche danger in 90% of cases. The forecast of avalanche danger was justified in 80% of cases. The artificial intelligence program helped the avalanche forecaster to improve the forecast quality. This method is currently being used for compiling an avalanche bulletin for two river basins in the Ile Alatau.

Artificial intelligence in radiodiagnosis assessment of circulatory disorders in cases of community-acquired pneumonia before and in the COVID-19 pandemic

Introduction. The COVID-19 pandemic not only caused a surge of viral pneumonia patients, but also opened up new opportunities in the field of radiology. One of the conditional «pluses» was the quantitative damage assessment of the lung parenchyma and microcirculation in COVID-19 using an artificial intelligence program. Objective. To evaluate the AI capabilities to detect the severity of anatomical and microcirculatory post-inflammatory disorders by CT and SPECT of pneumonic patients data. Materials and Methods. We analyzed prospective and retrospective data obtained by radiological studies of 187 patients with community-acquired pneumonia from 2006 to 2022 in the clinics of the St. Petersburg State Medical University named after acad. I.P. Pavlov. The follow-up period varied from 3 months to 8 years. The mean age of the patients was 34.3±9.2 years (w/m – 107/80). All patients underwent CT scan, a comprehensive functional examination of external respiration (KFIVD), SPECT. Results. The community-acquired pneumonia before the COVID-19 pandemic was generally characterized by signs of exudative bronchiolitis/bronchopneumonia, infectious viral alveolitis and pleuropneumonia. The first two forms in contrast to pleuropneumonia were associated with microcirculation disturbances. Signs of lung damage in COVID-19 had staging pattern based on morphological changes: edema, reticulation (increased severity of edema, cellular infiltration, intraalveolar fibrin), organization=consolidation (cellular infiltration, intraalveolar fibrin, fibroblast proliferation). Residual anatomical changes were accompanied by clinical symptoms (shortness of breath of varying severity, dry cough, weakness, intoxication). Conclusions. Using the artificial intelligence for post-processor CT and SPECT image processing seems to be relevant to assess the postinflammatory anatomical and microcirculatory disorders severity. Experience accumulation in combined AI and radiological study of patients with community-acquired pneumonia is capable to quantify residual anatomical and microcirculatory changes and useful for treatment tactics.

Clinical Implementation of a Combined Artificial Intelligence and Natural Language Processing Quality Assurance Program for Pulmonary Nodule Detection in the Emergency Department Setting.

This quality assurance study assessed the implementation of a combined artificial intelligence (AI) and natural language processing (NLP) program for pulmonary nodule detection in the emergency department setting. The program was designed to function outside of normal reading workflows to minimize radiologist interruption. In all, 19,246 CT examinations including at least some portion of the lung anatomy performed in the emergent setting from October 1, 2021, to June 1, 2022, were processed by the combined AI-NLP program. The program used an AI algorithm trained on 6-mm to 30-mm pulmonary nodules to analyze CT images and an NLP to analyze radiological reports. Cases flagged as negative for pulmonary nodules by the NLP but positive by the AI algorithm were classified as suspected discrepancies. Discrepancies result in secondary review of examinations for possible addenda. Out of 19,246 CT examinations, 50 examinations (0.26%) resulted in secondary review, and 34 of 50 (68%) reviews resulted in addenda. Of the 34 addenda, 20 patients received instruction for new follow-up imaging. Median time to addendum was 11 hours. The majority of reviews and addenda resulted from missed pulmonary nodules on CT examinations of the abdomen and pelvis. A background quality assurance process using AI and NLP helped improve the detection of pulmonary nodules and resulted in increased numbers of patients receiving appropriate follow-up imaging recommendations. This was achieved without disrupting in-shift radiologist workflow or causing significant delays in patient follow for the diagnosed pulmonary nodule.

Status of the Indian biopharmaceutical industry and way forward to be a dominant global player

Abstract The government promotes biotechnology in India, with the Department of Biotechnology (DBT) taking the leadership role, whereas some other departments also join in the endeavor. Modern biotechnology is no more new to India. Several therapeutic and diagnostic products are manufactured in the country from the primary stage. The emphasis is on the manufacture of patent-expired products more cost-effectively. The Indian biopharmaceutical market was approximately INR 32261 crores (US$ 4.032 billion) during 2021–2022 and approximately 1.14% of the global market. The manufacture of biopharmaceuticals needs sophisticated equipment and specialized skills. India has acquired skills in recombinant deoxyribonucleic acid (rDNA) technologies, developed capacities in genetically modifying Escherichia coli, Saccharomyces cerevisiae, Pichia pastoris, Hansenula polymorpha, and Chinese hamster ovary (CHO) cells lines, and has expertise in the multiplication of viruses in different animal and human cell lines. India produces many therapeutic and diagnostic recombinant proteins, including several therapeutic monoclonal antibodies. The more advanced areas of biopharmaceutical technology, such as mRNA-based cell therapies, including in-vitro modified somatic cell-based treatment and chimeric antigen receptor (CAR) T-cell therapy, are evolving. Genome editing technologies involving the manipulation of germ line cells are in the research and development (R&D) stage. Xenotransplantation technologies and the development of humanized transgenic animals are yet to start. Appropriate artificial intelligence programs may be included in the Indian biopharmaceutical industry to speed up the development of novel biopharmaceuticals. There are shortages of leaders adequately trained in biopharmaceutical skills in manufacturing, and the industry has constraints of abundant infrastructure and finance for deployment in R&D. India is committed to fulfilling a social objective by providing many essential medicines, including biopharmaceuticals at affordable prices through the Pradhan Mantri Bhartiya Janaushadhi Pariyojana.

Preliminary Exploration on the Application of 5G+VR/AR Scenario in Teaching - Taking the Lesson “Crossing the Currency Town” of Financial Literacy Education as an Example

The application of 5G technology in education is a major challenge in creating a new teaching ecosystem. The use of 5G+VR/AR scenarios in teaching is still in the theoretical exploration stage, lacking practical cases for education. This paper discusses the practical thinking of 5G+VR/AR scene intelligent teaching, providing cases of 5G+VR/AR classroom application to promote intelligent teaching in the direction of visualization, intelligence and scenario-based learning. Yizhou Primary School developed a curriculum resource “Crossing the Currency Town” that combines financial literacy teaching with 5G+VR/AR scenarios. The school built a 5G+VR smart cloud classroom based on the 5G network and VR/AR intelligent devices, educational terminals, and network devices to create an immersive and real-time teaching scene.
 The implementation of intelligent teaching requires the construction of an intelligent environment through the use of intelligent technology to improve the learners’ classroom experience and realize the interaction and cooperation between teachers, students, and machines. Intelligent equipment can also conduct multiple evaluations and monitor students’ learning effects in real-time, generating accurate analysis reports of the learning situation. The data collected can provide resources for personalized recommendation of intelligent teaching and generate intelligent and personalized learning programs.

Classification of the Traffic Status Subcategory with ETC Gantry Data: An Improved Support Tensor Machine Approach

Accurate and reliable traffic state identification is the prerequisite for developing intelligent traffic programs. With the improvement of intelligent traffic control measures, the traffic state of some highways has gradually stabilized. The current research on traffic state identification needs to fully meet the highly informative intelligent traffic system and traffic state subcategory analysis. To fill the gap above, we propose an improved support tensor machine (STM) method based on self-training and multiclassification for traffic state subcategory identification (ISTM) with ETC gantry data. This paper takes the excellent application of the support vector machine (SVM) in traffic state identification as the starting point of method design and extends to the STM. The ETC gantry data are represented as a third-order tensor model. This paper utilizes the similarity among tensor samples to construct the kernel function and recognize the traffic states. We simplify STM calculation with a one-against-one model and a self-training idea. An optimal fit of the characteristics is supplied by maximizing inter-subcategory tensor block distances and minimizing intra-subcategory tensor block distances throughout a joint utilization of the STM and multiscale training theories. The experiment in this paper uses ETC gantry data from the Jingtai highway in Shandong Province, and the findings reveal that the ISTM has optimum values of 0.2578 and 0.3254 for the SumD and 0.1718 and 0.1901 for the DBI as compared to K-mean clustering and the SVM. The ISTM trains the traffic state subcategory classifiers with high accuracy and strong generalization ability.

Towards The Review of Artificial Intelligence Programme Curriculum and Effective Collaborations among Academia for AI Programme Development in Africa

Abstract. Artificial Intelligence (AI) is an area of Computer Science that emphasizes the creation of intelligent systems that work, reasons and react like humans. In recent times information and computing solutions are leveraged on the concept of Artificial Intelligence with regard to Artificial Neural Network, Machine Learning, Fuzzy logic, Deep learning and Robotic Science. Applications of AI solutions have being highly effective in all sectors of human endeavors. During the Covid 19 pandemic, AI applications resulted in the rapid development and implementation of solutions which ordinarily would have taken years to achieve especially in the area of vaccine. Despite the gains of AI, we realized that less emphasis is placed in this trending area of computer science by the educational systems in Africa especially at university level. This research seeks to demand for an urgent need for a review of our educational curriculum to include AI and other trending disciplines in the frontiers of Information Technology. A multidisciplinary cloud based research model was also formulated which can be adopted in our educational system to foster multidisciplinary research. This will improve quality and efficient research outputs from universities in Africa. Keywords: Artificial Intelligence, Machine learning, Curriculum Review, AI Applications, academic-collaboration-models

Towards The Review of Artificial Intelligence Programme Curriculum and Effective Collaborations Among Academia for AI Programme Development in Africa

Artificial Intelligence (AI) is an area of Computer Science that emphasizes the creation of intelligent systems that work, reasons and react like humans. In recent times information and computing solutions are leveraged on the concept of Artificial Intelligence with regard to Artificial Neural Network, Machine Learning, Fuzzy logic, Deep learning and Robotic Science. Applications of AI solutions have being highly effective in all sectors of human endeavors. During the Covid 19 pandemic, AI applications resulted in the rapid development and implementation of solutions which ordinarily would have taken years to achieve especially in the area of vaccine. Despite the gains of AI, we realized that less emphasis is placed in this trending area of computer science by the educational systems in Africa especially at university level. This research seeks to demand for an urgent need for a review of our educational curriculum to include AI and other trending disciplines in the frontiers of Information Technology. A multidisciplinary cloud based research model was also formulated which can be adopted in our educational system to foster multidisciplinary research. This will improve quality and efficient research outputs from universities in Africa. Keywords: Artificial Intelligence, Machine learning, academic-collaboration-models, Curriculum Review, AI Applications

Chatbot As An Artificial Intelligence Program In Autonomous Learning In Nursing Students At A University In Piura, 2023

Objective: To determine the effects of chatbot as an artificial intelligence (AI) program on autonomous learning (ML) of nursing students at a private university in Piura, 2023. Method: quantitative, applied, experimental and pre-experimental approach. The population consisted of students of the Nursing Study Program and a sample of 32 students from the ninth cycle. A pretest and posttest were applied and 16 sessions were executed in the program. Results: according to the self-learning dimension with the pretest, 90.6% were placed in the low autonomy level, with the posttest 100% were located in the autonomous level. In self-regulation, the posttest presented 96.9%, establishing them at the medium level and low autonomy, with the posttest 100% are at the autonomous level. In metacognition, the pretest indicated that 78.1% were at the low level and at the posttest 100% were at the autonomous level. Conclusion: It was demonstrated that there is a significant influence of the program on the development of autonomous learning, accepting the alternative hypothesis.

THE EFFECTIVENESS OF ONLINE SERVICE AN INTELLIGENT LOOK-UP PROGRAM IN THE OCCUPATION AND CIVIC SERVICE OF TABALONG REGENCY

This study aims to describe the effectiveness of the E-KTP service in the Smart Pelanduk program at the Tabalong Regency Population and Civil Registration Service and to identify the inhibiting factors for the E-KTP service in the Smart Pelanduk program at the Tabalong Regency Population and Civil Registration Service. This study uses a qualitative research method with a qualitative descriptive approach with three data collection techniques, namely interviews, observation and documentation. The key informants in this study were the intelligent slow down team, village officials, and the community who participated in the smart slow down program. The data that has been collected will be analyzed using the analysis interactive model from Miles and Huberman.
 The results of this study indicate that based on the E-KTP Service of the Smart Penetrating Program for the accuracy of program targets, the socialization of the program and program objectives is appropriate and runs smoothly in accordance with the objectives of the intelligent rattlesnake program. Monitoring is still lacking so that program constraints are in vulnerable places. It was not resolved in place at the time of the implementation of the Smart Pelanduk Breakdown. Therefore, there were several factors and in the Smart Pelanduk Breakdown service program, namely non-periodic service, not all printing could be carried out on the spot, the officer who broke the smart trigger was different, then there were several villages for poor internet access. adequate. The results of this study suggest 1) Evaluation needs to be carried out for the sustainability of the intelligent slowdown program so that it can be minimized and even there are no more errors in service. So that the service can continue to run better. 2) The need for monitoring because to provide assurance of certainty and quality control so that the implementation of the program is always in the rules and provisions of the program that has been set.
 Keywords: Effectiveness, Service, E-KTP, Breaking Down Smart Program

Functional metagenomics uncovers nitrile-hydrolysing enzymes in a coal metagenome.

Introduction: Nitriles are the most toxic compounds that can lead to serious human illness through inhalation and consumption due to environmental pollution. Nitrilases can highly degrade nitriles isolated from the natural ecosystem. In the current study, we focused on the discovery of novel nitrilases from a coal metagenome using in silico mining. Methods: Coal metagenomic DNA was isolated and sequenced on the Illumina platform. Quality reads were assembled using MEGAHIT, and statistics were checked using QUAST. Annotation was performed using the automated tool SqueezeMeta. The annotated amino acid sequences were mined for nitrilase from the unclassified organism. Sequence alignment and phylogenetic analyses were carried out using ClustalW and MEGA11. Conserved regions of the amino acid sequences were identified using InterProScan and NCBI-CDD servers. The physicochemical properties of the amino acids were measured using ExPASy's ProtParam. Furthermore, NetSurfP was used for 2D structure prediction, while AlphaFold2 in Chimera X 1.4 was used for 3D structure prediction. To check the solvation of the predicted protein, a dynamic simulation was conducted on the WebGRO server. Ligands were extracted from the Protein Data Bank (PDB) for molecular docking upon active site prediction using the CASTp server. Results and discussion: In silico mining of annotated metagenomic data revealed nitrilase from unclassified Alphaproteobacteria. By using the artificial intelligence program AlphaFold2, the 3D structure was predicted with a per-residue confidence statistic score of about 95.8%, and the stability of the predicted model was verified with molecular dynamics for a 100-ns simulation. Molecular docking analysis determined the binding affinity of a novel nitrilase with nitriles. The binding scores produced by the novel nitrilase were approximately similar to those of the other prokaryotic nitrilase crystal structures, with a deviation of ±0.5.