When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?

Deep graph neural networks are extensively utilized to predict chemical reactivity and molecular properties. However, because of the complexity of chemical space, such models often have difficulty extrapolating beyond the chemistry contained in the training set. Augmenting the model with quantum mechanical (QM) descriptors is anticipated to improve its generalizability. However, obtaining QM descriptors often requires CPU-intensive computational chemistry calculations. To identify when QM descriptors help graph neural networks predict chemical properties, we conduct a systematic investigation of the impact of atom, bond, and molecular QM descriptors on the performance of directed message passing neural networks (D-MPNNs) for predicting 16 molecular properties. The analysis surveys computational and experimental targets, as well as classification and regression tasks, and varied data set sizes from several hundred to hundreds of thousands of data points. Our results indicate that QM descriptors are mostly beneficial for D-MPNN performance on small data sets, provided that the descriptors correlate well with the targets and can be readily computed with high accuracy. Otherwise, using QM descriptors can add cost without benefit or even introduce unwanted noise that can degrade model performance. Strategic integration of QM descriptors with D-MPNN unlocks potential for physics-informed, data-efficient modeling with some interpretability that can streamline de novo drug and material designs. To facilitate the use of QM descriptors in machine learning workflows for chemistry, we provide a set of guidelines regarding when and how to best leverage QM descriptors, a high-throughput workflow to compute them, and an enhancement to Chemprop, a widely adopted open-source D-MPNN implementation for chemical property prediction.

Virtual fit assessment of U.S. army body armor using NASA spacesuit techniques.

Fit and accommodation are critical design goals for a body armor system to maximize Soldiers' protection, comfort, mobility, and performance. The aim of this study is to assess fit and accommodation of body armor plates for the US Army. A virtual fit assessment technique, developed, validated, and deployed by NASA for spacesuit design, was adopted for this work. Specifically, 3D manikins of the Soldier population were overlaid virtually with geometrically similar surrogates of the armor plates. Trained subject matter experts with the US Army and NASA manually assessed the fit of the armor plates to manikins using a computer visualization tool and selected the appropriate plate size and position. A prediction model was built from the assessment data to predict the plate size from an arbitrary body shape and the resultant patterns of body-to-plate contact were quantified. The outcome indicated a unique trend of the plate sizes covarying with anthropometry. More pronouncedly, when the overlap between the body tissue and armor plate was quantified, female Soldiers are likely to experience a 25 times larger body-to-plate contact volume and 6.5 times larger contact depth than males on average, due to sex-based anthropometric differences. Overall, the prediction model and contact patterns provided key metrics for virtual body armor fit assessments, of which the locations, patterns, and magnitudes can help to improve sizing and fit of body armor systems, as previously demonstrated for NASA spacesuit design.

Epitranscriptomic mechanisms of androgen signalling and prostate cancer.

Prostate cancer (PCa) is the second most common cancer diagnosed in men. While radical prostatectomy and radiotherapy are often successful in treating localised disease, post-treatment recurrence is common. As the androgen receptor (AR) and androgen hormones play an essential role in prostate carcinogenesis and progression, androgen deprivation therapy (ADT) is often used to deprive PCa cells of the pro-proliferative effect of androgens. ADTs act by either blocking androgen biosynthesis (e.g. abiraterone) or blocking AR function (e.g. bicalutamide, enzalutamide, apalutamide, darolutamide). ADT is often effective in initially suppressing PCa growth and progression, yet emergence of castrate-resistant PCa and progression to neuroendocrine-like PCa following ADT are major clinical challenges. For this reason, there is an urgent need to identify novel approaches to modulate androgen signalling to impede PCa progression whilst also preventing or delaying therapy resistance. The mechanistic convergence of androgen and epitranscriptomic signalling offers a potential novel approach to treat PCa. The epitranscriptome involves covalent modifications of mRNA, notably, in the context of this review, the N(6)-methyladenosine (m6A) modification. m6A is involved in the regulation of mRNA splicing, stability, and translation, and has recently been shown to play a role in PCa and androgen signalling. The m6A modification is dynamically regulated by the METTL3-containing methyltransferase complex, and the FTO and ALKBH5 RNA demethylases. Given the need for novel approaches to treat PCa, there is significant interest in new therapies that target m6A that modulate AR expression and androgen signalling. This review critically summarises the potential benefit of such epitranscriptomic therapies for PCa patients.

10-Year Cumulative Incidence and Indications for Revision Total Joint Arthroplasty for Patients Who Have Ehlers-Danlos Syndrome.

BACKGROUND: Long-term complications following total joint arthroplasty are not well established for patients who have Ehlers-Danlos syndrome (EDS), a group of connective tissue disorders. This study compared 10-year incidence of revision surgery after total hip arthroplasty (THA) and total knee arthroplasty (TKA) in patients who have and do not have EDS. METHODS: A retrospective cohort analysis was conducted using a national all-payer claims database from 2010 to 2021 to identify patients who underwent primary TKA or THA. Patients who had and did not have EDS were propensity score-matched by age, sex, and a comorbidity index. Kaplan-Meier analyses and Cox proportional hazard models were used to determine the cumulative incidence and risks of revision experienced by patients who have and do not have EDS. RESULTS: The EDS patients who underwent TKA had a higher risk of all-cause revision (hazard ratio [HR]: 1.50, 95% confidence interval [95% CI]: 1.09 to 2.07, P < .014) and risk of revision due to instability (HR = 2.49, 95% CI: 1.37 to 4.52, P < .003). The EDS patients who underwent THA had a higher risk of all-cause revision (HR = 2.32, 95% CI: 1.47 to 3.65, P < .001), revision due to instability (HR = 4.26, 95% CI: 2.17 to 8.36, P < .001), and mechanical loosening (HR = 3.63, 95% CI: 2.05 to 6.44, P < .001). CONCLUSIONS: Patients who had EDS were found to have a higher incidence of revision within 10 years of undergoing TKA and THA compared to matched controls, especially for instability. Patients who have EDS should be counseled accordingly. Surgical technique and implant selection should include consideration for increased constraint in TKA and larger femoral heads or dual mobility articulations for THA.

Toward Accurate Quantum Mechanical Thermochemistry: (1) Extensible Implementation and Comparison of Bond Additivity Corrections and Isodesmic Reactions.

Obtaining accurate enthalpies of formation of chemical species, ΔHf, often requires empirical corrections that connect the results of quantum mechanical (QM) calculations with the experimental enthalpies of elements in their standard state. One approach is to use atomization energy corrections followed by bond additivity corrections (BACs), such as those defined by Petersson et al. or Anantharaman and Melius. Another approach is to utilize isodesmic reactions (IDRs) as shown by Buerger et al. We implement both approaches in Arkane, an open-source software that can calculate species thermochemistry using results from various QM software packages. In this work, we collect 421 reference species from the literature to derive ΔHf corrections and fit atomization energy corrections and BACs for 15 commonly used model chemistries. We find that both types of BACs yield similar accuracy, although Anantharaman- and Melius-type BACs appear to generalize better. Furthermore, BACs tend to achieve better accuracy than IDRs for commonly used model chemistries, and IDRs can be less robust because of the sensitivity to the chosen reference species and reactions. Overall, Anantharaman- and Melius-type BACs are our recommended approach for achieving accurate QM corrections for enthalpies.

Researching big IT in the UK National Health Service: A systematic review of theory-based studies.

OBJECTIVE: To identify and discuss theory-based studies of large-scale health information technology programs in the UK National Health Service. MATERIALS AND METHODS: Using the PRISMA systematic review framework, we searched Scopus, PubMed and CINAHL databases from inception to March 2022 for theory-based studies of large-scale health IT implementations. We undertook detailed full-text analyses of papers meeting our inclusion criteria. RESULTS: Forty-six studies were included after assessment for eligibility, of which twenty-five applied theories from the information systems arena (socio-technical approaches, normalization process theory, user acceptance theories, diffusion of innovation), twelve from sociology (structuration theory, actor-network theory, institutional theory), while nine adopted other theories. Most investigated England's National Program for IT (2002-2011), exploring various technologies among which electronic records predominated. Research themes were categorized into user factors, program factors, process outcomes, clinical impact, technology, and organizational factors. Most research was qualitative, often using a case study strategy with a longitudinal or cross-sectional approach. Data were typically collected through interviews, observation, and document analysis; sampling was generally purposive; and most studies used thematic or related analyses. Theories were generally applied in a superficial or fragmentary manner; and articles frequently lacked detail on how theoretical constructs and relationships aided organization, analysis, and interpretation of data. CONCLUSION: Theory-based studies of large NHS IT programs are relatively uncommon. As large healthcare programs evolve over a long timeframe in complex and dynamic environments, wider adoption of theory-based methods could strengthen the explanatory and predictive utility of research findings across multiple evaluation studies. Our review has confirmed earlier suggestions for theory selection, and we suggest there is scope for more explicit use of such theoretical constructs to strengthen the conceptual foundations of health informatics research. Additionally, the challenges of large national health informatics programs afford wide-ranging opportunities to test, refine, and adapt sociological and information systems theories.

Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation.

Detailed chemical kinetic models offer valuable mechanistic insights into industrial applications. Automatic generation of reliable kinetic models requires fast and accurate radical thermochemistry estimation. Kineticists often prefer hydrogen bond increment (HBI) corrections from a closed-shell molecule to the corresponding radical for their interpretability, physical meaning, and facilitation of error cancellation as a relative quantity. Tree estimators, used due to limited data, currently rely on expert knowledge and manual construction, posing challenges in maintenance and improvement. In this work, we extend the subgraph isomorphic decision tree (SIDT) algorithm originally developed for rate estimation to estimate HBI corrections. We introduce a physics-aware splitting criterion, explore a bounded weighted uncertainty estimation method, and evaluate aleatoric uncertainty-based and model variance reduction-based prepruning methods. Moreover, we compile a data set of thermochemical parameters for 2210 radicals involving C, O, N, and H based on quantum chemical calculations from recently published works. We leverage the collected data set to train the SIDT model. Compared to existing empirical tree estimators, the SIDT model (1) offers an automatic approach to generating and extending the tree estimator for thermochemistry, (2) has better accuracy and R2, (3) provides significantly more realistic uncertainty estimates, and (4) has a tree structure much more advantageous in descent speed. Overall, the SIDT estimator marks a great leap in kinetic modeling, offering more precise, reliable, and scalable predictions for radical thermochemistry.

Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates.

Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents. In this work, we generate a large set of data with the COSMO-RS method for over 28 000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (ΔΔG‡solv, ΔΔH‡solv) for a solution phase reaction. On unseen reactions, the model achieves mean absolute errors of 0.71 and 1.03 kcal mol-1 for ΔΔG‡solv and ΔΔH‡solv, respectively, relative to the COSMO-RS calculations. The model also provides reliable predictions of relative rate constants within a factor of 4 when tested on experimental data. The presented model can provide nearly instantaneous predictions of kinetic solvent effects or relative rate constants for a broad range of neutral closed-shell or free radical reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES strings.

Upregulated selenoprotein I during lipopolysaccharide-induced B cell activation promotes lipidomic changes and is required for effective differentiation into IgM-secreting plasma B cells.

The mechanisms driving metabolic reprogramming during B cell activation are unclear, particularly roles for enzymatic pathways involved in lipid remodeling. We found that murine B cell activation with lipopolysaccharide (LPS) led to a 1.6-fold increase in total lipids that included higher levels of phosphatidylethanolamine (PE) and plasmenyl PE. Selenoprotein I (SELENOI) is an ethanolamine phospholipid transferase involved in the synthesis of both PE and plasmenyl PE, and SELENOI expression was also upregulated during activation. Selenoi knockout (KO) B cells exhibited decreased levels of plasmenyl PE, which plays an important antioxidant role. Lipid peroxidation was measured and found to increase ∼2-fold in KO vs. wild-type (WT) B cells. Cell death was not impacted by KO in LPS-treated B cells and proliferation was only slightly reduced, but differentiation into CD138 + Blimp-1+ plasma B cells was decreased ∼2-fold. This led to examination of B cell receptors important for differentiation that recognize the ligand B cell activating factor, and levels of TACI (transmembrane activator, calcium-modulator, and cytophilin ligand interactor) (CD267) were significantly decreased on KO B cells compared with WT control cells. Vaccination with ovalbumin/adjuvant led to decreased ovalbumin-specific immunoglobulin M (IgM) levels in sera of KO mice compared with WT mice. Real-time polymerase chain reaction analyses revealed a decreased switch from surface to secreted IgM in spleens of KO mice induced by vaccination or LP-BM5 retrovirus infection. Overall, these findings detail the lipidomic response of B cells to LPS activation and reveal the importance of upregulated SELENOI for promoting differentiation into IgM-secreting plasma B cells.

Chemprop: A Machine Learning Package for Chemical Property Prediction.

Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approaches, directed message-passing neural networks (D-MPNNs) have proven to perform well on a variety of property prediction tasks. The software package Chemprop implements the D-MPNN architecture and offers simple, easy, and fast access to machine-learned molecular properties. Compared to its initial version, we present a multitude of new Chemprop functionalities such as the support of multimolecule properties, reactions, atom/bond-level properties, and spectra. Further, we incorporate various uncertainty quantification and calibration methods along with related metrics as well as pretraining and transfer learning workflows, improved hyperparameter optimization, and other customization options concerning loss functions or atom/bond features. We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level, and spectra functionality on a variety of property prediction data sets, including MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction of water-octanol partition coefficients, reaction barrier heights, atomic partial charges, and absorption spectra. Chemprop enables out-of-the-box training of D-MPNN models for a variety of problem settings in fast, user-friendly, and open-source software.

Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets.

Variability in case severity and in the range of symptoms experienced has been apparent from the earliest months of the COVID-19 pandemic. From a clinical perspective, symptom variability might indicate various routes/mechanisms by which infection leads to disease, with different routes requiring potentially different treatment approaches. For public health and control of transmission, symptoms in community cases were the prompt upon which action such as PCR testing and isolation was taken. However, interpreting symptoms presents challenges, for instance, in balancing the sensitivity and specificity of individual symptoms with the need to maximise case finding, whilst managing demand for limited resources such as testing. For both clinical and transmission control reasons, we require an approach that allows for the possibility of distinct symptom phenotypes, rather than assuming variability along a single dimension. Here we address this problem by bringing together four large and diverse datasets deriving from routine testing, a population-representative household survey and participatory smartphone surveillance in the United Kingdom. Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.

Assault-related anoxia and neck injuries in US emergency departments.

BACKGROUND: Early identification of non-fatal strangulation in the context of intimate partner violence (IPV) is crucial due to its severe physical and psychological consequences for the individual experiencing it. This study investigates the under-reported and underestimated burden of IPV-related non-fatal strangulation by analysing assault-related injuries leading to anoxia and neck injuries. METHODS: An IRB-exempt, retrospective review of prospectively collected data were performed using the National Electronic Injury Surveillance System All Injury Programme data from 2005 to 2019 for all assaults resulting in anoxia and neck injuries. The type and mechanism of assault injuries resulting in anoxia (excluding drowning, poisoning and aspiration), anatomical location of assault-related neck injuries and neck injury diagnosis by morphology, were analysed using statistical methods accounting for the weighted stratified nature of the data. RESULTS: Out of a total of 24 493 518 assault-related injuries, 11.6% (N=2 842 862) resulted from IPV (defined as perpetrators being spouses/partners). Among 22 764 cases of assault-related anoxia, IPV accounted for 40.4%. Inhalation and suffocation were the dominant mechanisms (60.8%) of anoxia, with IPV contributing to 41.9% of such cases. Neck injuries represented only 3.0% of all assault-related injuries, with IPV accounting for 21% of all neck injuries and 31.9% of neck contusions. CONCLUSIONS: The study reveals a significant burden of IPV-related anoxia and neck injuries, highlighting the importance of recognising IPV-related strangulation. Comprehensive screening for IPV should be conducted in patients with unexplained neck injuries, and all IPV patients should be screened for strangulation events.

Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back.

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. The second study exploited property models trained on the explored chemical space and previously reported molecules to discover nine top-performing molecules within a lightly explored structure-property space.

EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions.

Enzymatic reactions are an ecofriendly, selective, and versatile addition, sometimes even alternative to organic reactions for the synthesis of chemical compounds such as pharmaceuticals or fine chemicals. To identify suitable reactions, computational models to predict the activity of enzymes on non-native substrates, to perform retrosynthetic pathway searches, or to predict the outcomes of reactions including regio- and stereoselectivity are becoming increasingly important. However, current approaches are substantially hindered by the limited amount of available data, especially if balanced and atom mapped reactions are needed and if the models feature machine learning components. We therefore constructed a high-quality dataset (EnzymeMap) by developing a large set of correction and validation algorithms for recorded reactions in the literature and showcase its significant positive impact on machine learning models of retrosynthesis, forward prediction, and regioselectivity prediction, outperforming previous approaches by a large margin. Our dataset allows for deep learning models of enzymatic reactions with unprecedented accuracy, and is freely available online.

Role of Apparent Diffusion Coefficient Map-Based First- and High-Order Radiomic Features for the Discrimination of Sacral Chordomas and Chondrosarcomas With Overlapping Conventional Imaging Features.

PURPOSE: Chondrosarcomas arise from the lateral pelvis; however, midline chondrosarcomas (10%) display similar imaging features to chordoma, causing a diagnostic challenge. This study aims to determine the diagnostic accuracy of apparent diffusion coefficient (ADC)-based radiomic features and two novel diffusion indices for differentiating sacral chordomas and chondrosarcomas. METHODS: A retrospective, multireader review was performed of 82 pelvic MRIs (42 chordomas and 40 chondrosarcomas) between December 2014 and September 2021, split into training (n = 69) and validation (n = 13) data sets. Lesions were segmented on a single slice from ADC maps. Eight first-order features (minimum, mean, median, and maximum ADC, standard deviation, skewness, kurtosis, and entropy) and two novel indices: restriction index (RI, proportion of lesions with restricted diffusion) and facilitation index (FI, proportion of lesions with facilitated diffusion) were estimated. One hundred seven radiomic features comparing patients with chondrosarcoma versus chordoma were sorted based on mean group differences. RESULTS: There was good to excellent interobserver reliability for eight of the 10 ADC metrics on the training data set. Significant differences were observed (P < .005) for RI, FI, median, mean, and skewness using the training data set. Optimal cutpoints for diagnosis of chordoma were RI > 0.015; FI < 0.25; mean ADC < 1.7 × 10-3 mm2/s; and skewness >0.177. The optimal decision tree relied on FI. In a secondary analysis, significant differences (P < .00047) in chondrosarcoma versus chordoma were found in 18 of 107 radiomic features, including six first-order and 12 high-order features. CONCLUSION: The novel ADC index, FI, in addition to ADC mean, skewness, and 12 high-order radiomic features, could help differentiate sacral chordomas from chondrosarcomas.

ConfSolv: Prediction of Solute Conformer-Free Energies across a Range of Solvents.

Predicting Gibbs free energy of solution is key to understanding the solvent effects on thermodynamics and reaction rates for kinetic modeling. Accurately computing solution free energies requires the enumeration and evaluation of relevant solute conformers in solution. However, even after generation of relevant conformers, determining their free energy of solution requires an expensive workflow consisting of several ab initio computational chemistry calculations. To help address this challenge, we generate a large data set of solution free energies for nearly 44,000 solutes with almost 9 million conformers calculated in 41 different solvents using density functional theory and COSMO-RS and quantify the impact of solute conformers on the solution free energy. We then train a message passing neural network to predict the relative solution free energies of a set of solute conformers, enabling the identification of a small subset of thermodynamically relevant conformers. The model offers substantial computational time savings with predictions usually substantially within 1 kcal/mol of the free energy of the solution calculated by using computational chemical methods.

Evaluation of an Image-Derived Input Function for Kinetic Modeling of Nicotinic Acetylcholine Receptor-Binding PET Ligands in Mice.

Positron emission tomography (PET) radioligands that bind with high-affinity to α4ß2-type nicotinic receptors (α4ß2Rs) allow for in vivo investigations of the mechanisms underlying nicotine addiction and smoking cessation. Here, we investigate the use of an image-derived arterial input function and the cerebellum for kinetic analysis of radioligand binding in mice. Two radioligands were explored: 2-[18F]FA85380 (2-FA), displaying similar pKa and binding affinity to the smoking cessation drug varenicline (Chantix), and [18F]Nifene, displaying similar pKa and binding affinity to nicotine. Time-activity curves of the left ventricle of the heart displayed similar distribution across wild type mice, mice lacking the ß2-subunit for ligand binding, and acute nicotine-treated mice, whereas reference tissue binding displayed high variation between groups. Binding potential estimated from a two-tissue compartment model fit of the data with the image-derived input function were higher than estimates from reference tissue-based estimations. Rate constants of radioligand dissociation were very slow for 2-FA and very fast for Nifene. We conclude that using an image-derived input function for kinetic modeling of nicotinic PET ligands provides suitable results compared to reference tissue-based methods and that the chemical properties of 2-FA and Nifene are suitable to study receptor response to nicotine addiction and smoking cessation therapies.

Experimental Compilation and Computation of Hydration Free Energies for Ionic Solutes.

Although charged solutes are common in many chemical systems, traditional solvation models perform poorly in calculating solvation energies of ions. One major obstacle is the scarcity of experimental data for solvated ions. In this study, we release an experiment-based aqueous ionic solvation energy data set, IonSolv-Aq, that contains hydration free energies for 118 anions and 155 cations, more than 2 times larger than the set of hydration free energies for singly charged ions contained in the 2012 Minnesota Solvation Database commonly used in benchmarking studies. We discuss sources of systematic uncertainty in the data set and use the data to examine the accuracy of popular implicit solvation models COSMO-RS and SMD for predicting solvation free energies of singly charged ionic solutes in water. Our results indicate that most SMD and COSMO-RS modeling errors for ionic solutes are systematic and correctable with empirical parameters. We discuss two systematic offsets: one across all ions and one that depends on the functional group of the ionization site. After correcting for these offsets, solvation energies of singly charged ions are predicted using COSMO-RS to 3.1 kcal mol-1 MAE against a challenging test set and 1.7 kcal mol-1 MAE (about 3% relative error) with a filtered test set. The performance of SMD is similar, with MAE against those same test sets of 2.7 and 1.7 kcal mol-1. These results underscore the importance of compiling larger experimental data sets to improve solvation model parametrization and fairly assess performance.

RNA-based translation activators for targeted gene upregulation.

Technologies capable of programmable translation activation offer strategies to develop therapeutics for diseases caused by insufficient gene expression. Here, we present "translation-activating RNAs" (taRNAs), a bifunctional RNA-based molecular technology that binds to a specific mRNA of interest and directly upregulates its translation. taRNAs are constructed from a variety of viral or mammalian RNA internal ribosome entry sites (IRESs) and upregulate translation for a suite of target mRNAs. We minimize the taRNA scaffold to 94 nucleotides, identify two translation initiation factor proteins responsible for taRNA activity, and validate the technology by amplifying SYNGAP1 expression, a haploinsufficiency disease target, in patient-derived cells. Finally, taRNAs are suitable for delivery as RNA molecules by lipid nanoparticles (LNPs) to cell lines, primary neurons, and mouse liver in vivo. taRNAs provide a general and compact nucleic acid-based technology to upregulate protein production from endogenous mRNAs, and may open up possibilities for therapeutic RNA research.