Iterative Search Strategy Research Articles

Music Therapy for Pain and Anxiety Reduction in Pediatric Patients Undergoing Procedures: A Systematic Review and Meta-Analysis Protocol

Introduction: Many children undergo invasive and noninvasive procedures in the pediatric medical setting, which can be associated with pain and anxiety. Both pharmacological and nonpharmacological interventions are currently being used to minimize pain and anxiety in pediatric patients in the medical setting. Music therapy is an evidence-based, cost-effective, and nonpharmacological service administered by a board-certified music therapist which may reduce pediatric pain and anxiety during procedures. We will undertake a systematic review and meta-analysis that will comprehensively analyze the quantitative publications that detail the effects of music therapy on pediatric pain and anxiety for patients undergoing invasive or noninvasive medical procedures. Method: This protocol is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement and is registered in the International Prospective Register of Systematic Reviews (Prospero; CRD42023474635 ). A three-step, iterative search strategy will include (1) an initial scoping search, (2) development and execution of a comprehensive search strategy, and (3) a search for additional publications. Procedures for disagreements among reviewers and the article screening process are outlined. Statistical analyses including analysis of outcomes and sensitivity analysis has been determined. A plan for critical appraisal of sources using the Grading Recommendations Assessment, Development, and Evaluation (GRADE) and quality of evidence using the Outcome Reporting Bias in Trials (ORBIT) of evidence is also present in this protocol. Conclusion: This protocol is presented with the intention to execute and publish results of this systematic review and meta-analysis to help inform clinical practice and future research.

A bibliometric performance analysis of publication productivity within pedagogy, andragogy, and heutagogy continuum: Outcomes of SciVal analytics

The pedagogy, andragogy, and heutagogy (PAH) continuum have been widely endorsed within higher education and online learning over the years. Thus, the evaluation of research productivity has become relevant due to the emerging development of the PAH continuum as a theoretical framework. This paper aims to explore the research productivity within the PAH continuum by performing a bibliometric performance analysis. An iterative search strategy was performed to identify PAH authors and then investigate their research productivity over a 5-year period (2017–2022) using the SciVal analytics instrument by Elsevier. Drawing upon the results from multiple bibliometric analyses, the study explores publication productivity specifically in relation to the PAH continuum. This study revealed that distance education, learning, teaching, professional development, and lifelong learning have been the most prominent research areas within the PAH continuum. Within that, related topics were identified, including online learning, mobile learning, e-learning, and educational technology.

Understanding how therapeutic exercise prescription changes outcomes important to patients with persistent non-specific low back pain: a realist review protocol

IntroductionPersistent low back pain (LBP) is the leading cause of disability worldwide, and therapeutic exercise is recommended as a first-line treatment in international guidelines. The effects of exercise on clinical outcomes of pain and physical function are small to moderate, despite broader impacts on cardiovascular systems, biological health, mood, and emotional well-being. Therapeutic exercise prescription is defined as exercise that is prescribed by a clinician for a health-related treatment. It is unknown how therapeutic exercise prescription creates effects on outcomes of importance. Realist reviews explore how underlying mechanisms (M) may be active in the context (C) of certain situations, settings, or populations to create an intended or unintended outcome (O). Our objective is to explore and understand the mechanisms by which therapeutic exercise prescription changes outcomes for people with persistent LBP.MethodsWe will develop initial programme theories based on preliminary data from a previous systematic review and consensus workshop. These theories will be modified with input from a steering group (experts), a stakeholder group (people with lived experience of exercise for persistent LBP and clinicians), and a scoping search of the published literature. An information specialist will design and undertake an iterative search strategy. These will be used to create CMO configurations, which will be refined and tested using the literature. The realist review will be reported following RAMESES guidance.DiscussionRealist reviews are uncommon in LBP research to date, yet those offer an opportunity to contrast with traditional methods of randomised controlled trials and systematic reviews and provide additional information regarding the contexts and mechanisms that may trigger certain outcomes. This can aid our understanding of the contextual features that may influence exercise prescription, such as for whom they are most effective, in what setting, how they are implemented and why. This realist synthesis will enhance our understanding of therapeutic exercise prescription to improve adherence and engagement and ultimately will provide clinically relevant recommendations regarding exercise prescription for those with persistent LBP.Systematic review registrationThe review has been registered with PROSPERO (CRD42017072023).

Contexts, behavioural mechanisms and outcomes to optimise therapeutic exercise prescription for persistent low back pain: a realist review

ObjectiveTherapeutic exercises are a core treatment for low back pain (LBP), but it is uncertain how rehabilitative exercise facilitates change in outcomes. Realist reviews explore how the context (C) of...

Short-term peak-shaving operation of “N-reservoirs and multicascade” large-scale hydropower systems based on a decomposition-iteration strategy

The boom in “N-reservoirs-and-multicascade” large-scale hydropower systems (N-M-LSHSs) poses tremendous challenges to the short-term peak-shaving (STPS) operation of the power grid, which includes modeling difficulties, complex constraint-handling and high time-consumption. A decomposition and iterative search strategy (DISS) for STPS operation of N-M-LSHS is proposed to overcome these challenges. First, a decomposition strategy based on adjustability is used to divide an N-M-LSHS into several independent hydropower subsystems (hydrosubs), and a model based on this strategy is formulated to restructure the coupling connections of the N-M-LSHS. Second, a method for generating an initial procedure is used to determine the power output of all hydrosubs, where mixed-integer linear programming (MILP) is selected to solve each hydrosub to enhance the search efficiency. Finally, an iterative search procedure is developed to converge to an acceptable solution within a suitable time frame by dynamically updating the peak-valley differences of the residual load (PVDRL). The DISS is tested for the eleven cascade hydropower stations on the Lancang River in Southwest China. The simulation results show that the DISS can effectively improve the solution efficiency by guaranteeing the accuracy requirements and the optimized PVDRL for the dry and wet seasons is reduced by 55.28 % and 75.51 %, respectively.

An evolutionary multi-algorithm based framework for the parametric estimation of proton exchange membrane fuel cell

This paper proposes a multi-strategy, multiple algorithms based hybrid strategy using flower pollination algorithm (FPA), grey wolf optimizer (GWO), INFO, and naked mole rat algorithm (NMRA). The proposed algorithm, named the Flower Grey INFO Naked (FGIN) algorithm, incorporates the most effective equations from the FPA, GWO, INFO, and NMRA. Here FPA’s basic structure following global and local search is used, GWO is meant for providing extensive exploration, whereas INFO and NMRA both contribute towards exploitative search. Dynamic iterative search and population segmentation strategies are incorporated for enhanced performance of FGIN algorithm. For enhanced self-adaptivity, six mutation weight operators are applied to the three parameters of the proposed strategy. FGIN is also subjected to higher dimensional and variable population analysis, and has been found to be highly effective. A deeper analysis using CEC 2005, CEC 2017, CEC 2019 and CEC 2022 benchmark data set is also performed to validate the superiority of the proposed algorithm with respect to success history based differential evolution (SHADE), self-adaptive DE (SaDE), NL-LSHADE-LBC, DE with active archive (JADE), LSHADE-SPACMA, evolutionary algorithms with eigen crossover (EA4eig), extended GWO (GWO-E), NL-LSHADE-RSP-MID, jDE100, and others. The proposed FIGN algorithm is then used for the parametric identification of proton exchange membranes in fuel cells (PEMFC). The optimization challenge of PEMFC is to minimize the sum of squared error (SSE) between the experimental and measured voltage. And also determine, the optimal values of seven unknown parameters for the PEMFC stack’s. To illustrate the potential of the FGIN algorithm is validated by utilizing five well-known commercial PEMFCs, namely NedStack PS6, BCS 500 W, Stack 250 W, Ballard Mark V, and Horizon H-12 Stack. In order to check the effectiveness of the FGIN algorithm, both parametric and non-parametric statistical tests have been conducted, and it has been found that the proposed algorithm performs significantly better.

Global iterative optimization framework for predicting cognitive function statuses of patients with end‐stage renal disease

AbstractThe existing methods to determine the cognitive function level of end‐stage renal disease (ESRD) are not only inaccurate but also susceptible to the influence of the patient's education level, emotional state, and examination environment. We proposed a global iterative optimization framework (GIO) to accurately predict cognitive function statuses of ESRD patients without being affected by the above factors. First, the functional magnetic resonance imaging (fMRI) data preprocessed and the time series were extracted to construct brain functional networks. Secondly, the areas under curve (AUC) of topological attribute parameters in the brain functional networks were extracted as features. After statistical analysis, the global efficiency, the characteristic path length, and the shortest path length in the small‐world network were selected as features for linear fusion. Finally, the support vector regression (SVR) was used as the basis of GIO framework, and the global iterative search strategy was introduced to find out the most appropriate penalty factor and radial basis function parameters. Achieving the objective of accurately predicting the cognitive function status of patients with end‐stage renal disease. Since this framework uses SVR which has special advantages in processing small sample data, and introduces appropriate parameter selection method, the prediction ability of GIO framework has been significantly improved. Experimental results demonstrate that the final prediction accuracy of the proposed framework is significantly better than those of SVR, LSSVR, GMO‐SVR, GMO‐LSSVR, and PSO‐SVR. The mean absolute error (MAE) and the mean absolute percentage error (MAPE) of the proposed framework are 0.55% and 2.58%, respectively. It is suggested that this framework is more convenient to determine the current level of cognitive function in ESRD patients and is conducive to the early prevention of cognitive dysfunction in ESRD patients.

AGEISM IN ARTIFICIAL INTELLIGENCE: A REVIEW

Abstract Background Artificial intelligence (AI) has emerged as a major driver of technological development in the 21st century, yet little attention has been paid to algorithmic biases towards older adults. "Digital ageism" is a new form of ageism that is embedded into technology and AI systems. Aim: This review aimed to explore how age-related bias is encoded in AI systems to better understand digital ageism. Methods The scoping review follows a six-stage methodology framework developed by Arksey and O'Malley. The search strategy has been established in six databases and we will investigate grey literature databases, targeted websites, popular search engines. An iterative search strategy was used. Studies meet the inclusion criteria if they are in English, peer-reviewed, available electronically in full-text, and included the concepts ‘bias’ and old age. At least two reviewers independently conducted title/abstract screening and full-text screening. Results Our database searches resulted in 7 595 manuscripts that underwent title and abstract screening. Of these 49 papers, were included in the study. The word "ageism" was explicitly mentioned only in about half of these papers. Approximately half the papers mentioned how age-related bias could be encoded into AI systems. The most commonly used AI applicaiton was computer vision. Conclusions Our preliminary findings contribute foundational knowledge about the age-related biases that were encoded or amplified in AI systems. This work advances how AI can be developed in a manner consistent with ethical values and human rights legislation, particularly as it relates to an older and aging population.

FISS: A Trajectory Planning Framework Using Fast Iterative Search and Sampling Strategy for Autonomous Driving

Trajectory planning is a critical component in autonomous vehicles directly responsible for driving safety and efficiency during deployment. The ability to find the optimal trajectory in real-time is critical for autonomous driving. This paper presents a novel general framework using the Fast Iterative Search and Sampling (FISS) strategy for sampling-based trajectory planning, which can find the optimal trajectory from an enormous number of candidates with high efficiency in real-time. Specifically, before generating any trajectories, the proposed method utilizes historical planning results as prior information in heuristics to estimate the cost distribution over the sampling space. On this basis, the Fast Iterative Search and Sampling strategy is employed to explore the sampling space for possible candidates and generate trajectories for verification during the search process. Experimental results show that our method can significantly outperform existing frameworks by order of magnitude in planning efficiency while ensuring safety and maintaining high accuracy.

A bibliometric performance analysis of publication productivity in the corporate social responsibility field: Outcomes ofSciValanalytics

AbstractCorporate social responsibility (CSR) research in academia has been increasing over time, especially in recent years. Thus, the evaluation of relative research productivity is becoming increasingly relevant due to the current importance of this topic. The paper aims to investigate research productivity in the CSR field by performing a bibliometric analysis. An iterative search strategy was used to firstly identify productive CSR authors and then examine their productivity over a 5‐year period (2015–2020) using the SciVal tool by Elsevier. Drawing on the results from different bibliometric analyses, the study investigates quantitative and qualitative publication performance at the country (macro), institutional (meso) and individual (micro) levels. This study is the first bibliometric analysis on CSR that is not related to a specific journal and has a multi‐level nature by providing the groundwork in determining the knowledge structure of CSR.

Finite Position Control of Interior Permanent Magnet Synchronous Motors at Low Speed

This article presents a new scheme to extract the rotor position angle of interior permanent magnet synchronous motors using a high-frequency signal injection method with an iterative strategy. An iterative strategy based on the binary principle is proposed to reduce the computational burden using discrete rotor position angles. These discrete rotor position angles are obtained from the angle space, which is divided into several separate sectors and employed to calculate the defined cost function that generates the optimal rotor position angle. With the number of iterations increasing, the accuracy of the iterative search strategy will increase geometrically. Finally, a series of experimental tests have been carried out to compare the performances of the proposed and the conventional low-speed sensorless methods. Experimental results have verified the effectiveness of the proposed low-speed sensorless scheme.

Systematic review of guidelines for managing physical health during treatment for substance use disorders: Implications for the alcohol and other drug workforce.

IssuesSubstance use disorders are associated with significant physical health comorbidities, necessitating an integrated treatment response. However, service fragmentation can preclude the management of physical health problems during addiction treatment. The aim of this systematic review was to synthesise the recommendations made by clinical practice guidelines for addressing the physical health of people attending alcohol and other drug (AOD) treatment.ApproachAn iterative search strategy of grey literature sources was conducted from September 2020 to February 2021 to identify clinical practice guidelines. Content pertaining to physical health care during AOD treatment was extracted. Quality of guidelines were appraised using the Appraisal of Guidelines Research and Evaluation II (AGREE‐II) tool.FindingsThirty‐three guidelines were included for review. Fourteen guidelines were considered high quality based on AGREE‐II scores. Neurological conditions (90.9%) and hepatitis (81.8%) were the most frequent health problems addressed. Most guidelines recommended establishing referral pathways to address physical health comorbidities (90.9%). Guidance on facilitating these referral pathways was less common (42.4%). Guidelines were inconsistent in their recommendations related to oral health, tobacco use, physical activity, nutrition and the use of standardised assessment tools.Implications and ConclusionsGreater consistency and specificity in the recommendations made for integrating physical health care within addiction treatment is needed. Ensuring that recommendations are applicable to the AOD workforce and to treatment services limited by funding and resource constraints should enhance implementation. Future guideline development groups should consider increased consultation with the AOD workforce and inclusion of clinical tools and decision aids to facilitate referral pathways.

A Design of the Ecotourism Individualized Route Planning System Based on the Ecological Footprint Model.

In this study, aiming at the huge amount of information in the tourism field, the pressure of ecological environment, and tedious personalized route planning, an ecotourism personalized route planning system based on the ecological footprint model is designed. In order to recommend routes that meet the time limit and the starting and ending points of the user's choice, the tourist route recommendation problem is studied as a directional problem on the basis of comprehensively considering the popularity of scenic spots and the user's interest preferences as the scenic spots score. The scenic spot scoring strategy is scenic spot scoring, and the iterative local search strategy is used to plan tourist routes according to the optimization goal with the largest route score, and improve the real tourist routes, food, and accommodation strategies. On the basis of launched tourist routes, they recommend tourists' favorite food and accommodation. The model finally completes the fine arrangement of scenic spots, food, and accommodation in the whole tourist route. The system test results show that the system has obvious advantages in personalized path planning effect, excellent user feedback effect, and certain application value.

Ageism and Artificial Intelligence: Protocol for a Scoping Review

BackgroundArtificial intelligence (AI) has emerged as a major driver of technological development in the 21st century, yet little attention has been paid to algorithmic biases toward older adults.ObjectiveThis paper documents the search strategy and process for a scoping review exploring how age-related bias is encoded or amplified in AI systems as well as the corresponding legal and ethical implications.MethodsThe scoping review follows a 6-stage methodology framework developed by Arksey and O’Malley. The search strategy has been established in 6 databases. We will investigate the legal implications of ageism in AI by searching grey literature databases, targeted websites, and popular search engines and using an iterative search strategy. Studies meet the inclusion criteria if they are in English, peer-reviewed, available electronically in full text, and meet one of the following two additional criteria: (1) include “bias” related to AI in any application (eg, facial recognition) and (2) discuss bias related to the concept of old age or ageism. At least two reviewers will independently conduct the title, abstract, and full-text screening. Search results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guideline. We will chart data on a structured form and conduct a thematic analysis to highlight the societal, legal, and ethical implications reported in the literature.ResultsThe database searches resulted in 7595 records when the searches were piloted in November 2021. The scoping review will be completed by December 2022.ConclusionsThe findings will provide interdisciplinary insights into the extent of age-related bias in AI systems. The results will contribute foundational knowledge that can encourage multisectoral cooperation to ensure that AI is developed and deployed in a manner consistent with ethical values and human rights legislation as it relates to an older and aging population. We will publish the review findings in peer-reviewed journals and disseminate the key results with stakeholders via workshops and webinars.Trial RegistrationOSF Registries AMG5P; https://osf.io/amg5pInternational Registered Report Identifier (IRRID)DERR1-10.2196/33211

A Hybrid Optimization Method for Manufacturing Cell Scheduling with Random Interruptions Based on Improved Wolf Pack Algorithm and Simulation

Cell scheduling is an important issue in the cell manufacturing system. Considering the problems of machine interruption and energy consumption, this paper establishes a stochastic optimization model to minimize the makespan as well as the cost of energy consumption during machine idling and the interruption cost. A hybrid algorithm, based on the wolf pack algorithm (WPA) and simulation, is proposed to solve the problem. In order to deal with the problems of slow convergence speed and easily falling into a local optimum which often happens for the standard WPA, the iterative local search strategy, Cauchy mutation strategy, and Gaussian disturbance are introduced into the search process of WPA to improve its performance. The solution with stochastic parameters is evaluated via simulation. Numerical experiments demonstrate that the proposed hybrid algorithm shows a good performance in both solution quality and convergence speed, and a satisfactory solution to the problem can be reached within a reasonable number of iterations.

A Two-Stage Cooperative Evolutionary Algorithm With Problem-Specific Knowledge for Energy-Efficient Scheduling of No-Wait Flow-Shop Problem.

Green scheduling in the manufacturing industry has attracted increasing attention in academic research and industrial applications with a focus on energy saving. As a typical scheduling problem, the no-wait flow-shop scheduling has been extensively studied due to its wide industrial applications. However, energy consumption is usually ignored in the study of typical scheduling problems. In this article, a two-stage cooperative evolutionary algorithm with problem-specific knowledge called TS-CEA is proposed to address energy-efficient scheduling of the no-wait flow-shop problem (EENWFSP) with the criteria of minimizing both makespan and total energy consumption. In TS-CEA, two constructive heuristics are designed to generate a desirable initial solution after analyzing the properties of the problem. In the first stage of TS-CEA, an iterative local search strategy (ILS) is employed to explore potential extreme solutions. Moreover, a hybrid neighborhood structure is designed to improve the quality of the solution. In the second stage of TS-CEA, a mutation strategy based on critical path knowledge is proposed to extend the extreme solutions to the Pareto front. Moreover, a co-evolutionary closed-loop system is generated with ILS and mutation strategies in the iteration process. Numerical results demonstrate the effectiveness and efficiency of TS-CEA in solving the EENWFSP.

Harm minimisation for the management of self-harm: a mixed-methods analysis of electronic health records in secondary mental healthcare.

Prevalence of self-harm in the UK was reported as 6.4% in 2014. Despite sparse evidence for effectiveness, guidelines recommend harm minimisation; a strategy in which people who self-harm are supported to do so safely. To determine the prevalence, sociodemographic and clinical characteristics of those who self-harm and practise harm minimisation within a London mental health trust. We included electronic health records for patients treated by South London and Maudsley NHS Trust. Using an iterative search strategy, we identified patients who practise harm minimisation, then classified the approaches using a content analysis. We compared the sociodemographic characteristics with that of a control group of patients who self-harm and do not use harm minimisation. In total 22 736 patients reported self-harm, of these 693 (3%) had records reporting the use of harm-minimisation techniques. We coded the approaches into categories: (a) 'substitution' (>50% of those using harm minimisation), such as using rubber bands or using ice; (b) 'simulation' (9%) such as using red pens; (c) 'defer or avoid' (7%) such as an alternative self-injury location; (d) 'damage limitation' (9%) such as using antiseptic techniques; the remainder were unclassifiable (24%). The majority of people using harm minimisation described it as helpful (>90%). Those practising harm minimisation were younger, female, of White ethnicity, had previous admissions and were less likely to have self-harmed with suicidal intent. A small minority of patients who self-harm report using harm minimisation, primarily substitution techniques, and the large majority find harm minimisation helpful. More research is required to determine the acceptability and effectiveness of harm-minimisation techniques and update national clinical guidelines.

RLIM: a recursive and latent infection model for the prediction of US COVID-19 infections and turning points.

Initially found in Hubei, Wuhan, and identified as a novel virus of the coronavirus family by the WHO, COVID-19 has spread worldwide at exponential speed, causing millions of deaths and public fear. Currently, the USA, India, Brazil, and other parts of the world are experiencing a secondary wave of COVID-19. However, the medical, mathematical, and pharmaceutical aspects of its transmission, incubation, and recovery processes are still unclear. The classical susceptible–infected–recovered model has limitations in describing the dynamic behavior of COVID-19. Hence, it is necessary to introduce a recursive, latent model to predict the number of future COVID-19 infection cases in the USA. In this article, a dynamic recursive and latent infection model (RLIM) based on the classical SEIR model is proposed to predict the number of COVID-19 infections. Given COVID-19 infection and recovery data for a certain period, the RLIM is able to fit current values and produce an optimal set of parameters with a minimum error rate according to actual reported numbers. With these optimal parameters assigned, the RLIM model then becomes able to produce predictions of infection numbers within a certain period. To locate the turning point of COVID-19 transmission, an initial value for the secondary infection rate is given to the RLIM algorithm for calculation. RLIM will then calculate the secondary infection rates of a continuous time series with an iterative search strategy to speed up the convergence of the prediction outcomes and minimize the maximum square errors. Compared with other forecast algorithms, RLIM is able to adapt the COVID-19 infection curve faster and more accurately and, more importantly, provides a way to identify the turning point in virus transmission by searching for the equilibrium between recoveries and new infections. Simulations of four US states show that with the secondary infection rate omega initially set to 0.5 within the selected latent period of 14 days, RLIM is able to minimize this value at 0.07 and reach an equilibrium condition. A successful forecast is generated using New York state’s COVID-19 transmission, in which a turning point is predicted to emerge on January 31, 2021.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11071-021-06520-1.

Framing action to reduce health inequalities: what is argued for through use of the 'upstream-downstream' metaphor?

BackgroundPublic health insights struggle to compete with dominant ideas which frame health inequalities as a problem of individual behaviour. There is consequently a need to critically reflect upon and question the effectiveness of different strategies for framing and communicating key insights. Taking the example of the ‘upstream–downstream’ metaphor, this literature review contributes to a necessary first step by asking what exactly is being argued for through its use.MethodsAn iterative search strategy was used to identify peer-reviewed articles which could contribute to the review question. A discourse analysis framework informed data extraction and synthesis of 24 articles. Articles were subsequently categorized into groups which reflected the different uses of the metaphor identified.ResultsAll authors used the metaphor to promote a particular causal understanding of health inequalities, leading some to recommend policies and programmes, and others to focus on implementation processes. This seemingly simple metaphor has evolved beyond differentiating ‘upstream’ from ‘downstream’ determinants, to communicate an ambitious politically engaged agenda for change.ConclusionsThe metaphor is not without its critics and in light of the complexity of the arguments encapsulated in its use, work is needed to establish if it can, and does, resonate as intended with wider audiences.

Artificial Intelligence-Guided De Novo Molecular Design Targeting COVID-19.

An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. While computational docking simulations remain a popular method of choice for the in silico ligand design and high-throughput screening of therapeutic agents, it is severely limited in the discovery of new candidate ligands owing to the high computational cost and vast chemical space. Here, we present a de novo molecular design strategy that leverages artificial intelligence (AI) to discover new therapeutic agents against SARS-CoV-2. A Monte Carlo tree search algorithm combined with a multitask neural network surrogate model for expensive docking simulations, and recurrent neural networks for rollouts, is used in an iterative search and retrain strategy. Using Vina scores as the target objective to measure binding to either the isolated spike protein (S-protein) at its host receptor region or to the S-protein/angiotensin converting enzyme 2 receptor interface, we generate several (∼100’s) new therapeutic agents that outperform Food and Drug Administration (FDA) (∼1000’s) and non-FDA molecules (∼million). Our AI strategy is broadly applicable for accelerated design and discovery of chemical molecules with any user-desired functionality.