Experience of "One Stop TB Diagnostic Solution" Model in Engaging a Private Laboratory for End-to-End Diagnostic Services in the National TB Elimination Program in Hisar, India.

The complete diagnostic evaluation of tuberculosis based on its drug-resistance profile is critical for appropriate treatment decisions. The TB diagnostic landscape in India has been transformed with the scaling-up of WHO-recommended diagnostics, but challenges remain with specimen transportation, completing diagnostic assessment, turnaround time (TAT), and maintaining laboratories. Private laboratories have demonstrated efficiencies for specimen collection, transportation, and the timely testing and issue of results. A one-stop TB diagnostic model was designed to assess the feasibility of providing end-to-end diagnostic services in the Hisar district of Haryana state, India. A NTEP-certified private laboratory was engaged to provide the services, complementing the existing public sector diagnostic services. A total of 10,164 specimens were collected between May 2022 and January 2023 and these were followed for the complete diagnostic assessment of Drug-Susceptible TB (DS-TB) and Drug-Resistant TB (DR-TB) and the time taken for issuing results. A total of 2152 (21%) patients were detected with TB, 1996 (93%) Rifampicin-Sensitive and 134 (6%) with Rifampicin-Resistant TB. Nearly 99% of the patients completed the evaluation of DS-TB and DR-TB within the recommended TAT. The One-Stop TB/DR-TB Diagnostic Solution model has demonstrated that diagnostic efficiencies could be enhanced through the strategic purchase of private laboratory services.

The potential impact of vaccination on tuberculosis burden in India: A modelling analysis.

Background & objectives: Vaccination will play an important role in meeting the end tuberculosis (TB) goals. While certain vaccine candidates in advanced stages of clinical trials raise hope for the future availability of new tools, in the immediate term, there is also increasing interest in Bacille Calmette-Guérin revaccination among adults and adolescents as a potential strategy. Here, we sought to estimate the potential epidemiological impact of TB vaccination in India. Methods: We developed a deterministic, age-structured, compartmental model of TB in India. Data from the recent national prevalence survey was used to inform epidemiological burden while also incorporating a vulnerable population who may be prioritized for vaccination, the latter consistent with the burden of undernutrition. Using this framework, the potential impact on incidence and mortality of a vaccine with 50 per cent efficacy was estimated, if rolled out in 2023 to cover 50 per cent of the unvaccinated each year. Simulated impacts were compared for disease- vs. infection-preventing vaccines, as well as when prioritizing vulnerable groups (those with undernutrition) rather than the general population. A sensitivity analyses were also conducted with respect to the duration, and efficacy, of vaccine immunity. Results: When rolled out in the general population, an infection-preventing vaccine would avert 12 per cent (95% Bayesian credible intervals (Crl): 4.3-28%) of cumulative TB incidence between 2023 and 2030, while a disease-preventing vaccine would avert 29 per cent (95% Crl: 24-34%). Although the vulnerable population accounts for only around 16 per cent of India's population, prioritizing this group for vaccination would achieve almost half the impact of rollout in the general population, in the example of an infection-preventing vaccine. Sensitivity analysis also highlights the importance of the duration and efficacy of vaccine-induced immunity. Interpretation & conclusions: These results highlight how even a vaccine with moderate effectiveness (50%) could achieve substantial reductions in TB burden in India, especially when prioritized for the most vulnerable.

A comparative study on effect of news sentiment on stock price prediction with deep learning architecture.

The accelerated progress in artificial intelligence encourages sophisticated deep learning methods in predicting stock prices. In the meantime, easy accessibility of the stock market in the palm of one's hand has made its behavior more fuzzy, volatile, and complex than ever. The world is looking at an accurate and reliable model that uses text and numerical data which better represents the market's highly volatile and non-linear behavior in a broader spectrum. A research gap exists in accurately predicting a target stock's closing price utilizing the combined numerical and text data. This study uses long short-term memory (LSTM) and gated recurrent unit (GRU) to predict the stock price using stock features alone and incorporating financial news data in conjunction with stock features. The comparative study carried out under identical conditions dispassionately evaluates the importance of incorporating financial news in stock price prediction. Our experiment concludes that incorporating financial news data produces better prediction accuracy than using the stock fundamental features alone. The performances of the model architecture are compared using the standard assessment metrics -Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Correlation Coefficient (R). Furthermore, statistical tests are conducted to further verify the models' robustness and reliability.

Implementation of a tuberculosis elimination project, India 2018-2019.

Objective: To describe the changes in tuberculosis case notifications by the private sector after implementation of the Joint Effort for Elimination of Tuberculosis project in India in 2018. Methods: We retrieved data from the project recorded in India's national tuberculosis surveillance system. We analysed data on 95 project districts in six states (Andhra Pradesh, Himachal Pradesh, Karnataka, Punjab including Chandigarh, Telangana and West Bengal) to assess changes in the number of tuberculosis notifications, private provider notifiers and microbiological confirmations of cases from 2017 (baseline) to 2019. We compared case notification rates in districts where the project was implemented with the rates in districts where it was not. Findings: From 2017 to 2019, tuberculosis notifications increased by 138.1% (from 44 695 to 106 404), and case notification rates more than doubled from 20 to 44 per 100 000 population. The number of private notifiers increased by over threefold, from 2912 to 9525, during this period. The number of microbiologically confirmed pulmonary and extra-pulmonary tuberculosis cases notified increased by more than two times (from 10 780 to 25 384) and nearly three times (from 1477 to 4096), respectively. The districts where the project was implemented showed a 150.3% increase in case notification rates per 100 000 population from 2017 to 2019 (from 16.8 to 41.9) while in non-project districts, this increase was only 89.8% (from 6.1 to 11.6). Conclusion: The substantial increase in tuberculosis notifications demonstrate the value of the project in engaging the private sector. Scaling up these interventions is important to consolidate and extend these gains towards tuberculosis elimination.

Machine Learning Models for Predicting Molecular UV-Vis Spectra with Quantum Mechanical Properties.

Accurate understanding of ultraviolet-visible (UV-vis) spectra is critical for the high-throughput synthesis of compounds for drug discovery. Experimentally determining UV-vis spectra can become expensive when dealing with a large quantity of novel compounds. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning methods. In this work, we use both quantum mechanically (QM) predicted and experimentally measured UV-vis spectra as input to devise four different machine learning architectures, UVvis-SchNet, UVvis-DTNN, UVvis-Transformer, and UVvis-MPNN, and assess the performance of each method. We find that the UVvis-MPNN model outperforms the other models when using optimized 3D coordinates and QM predicted spectra as input features. This model has the highest performance for predicting UV-vis spectra with a training RMSE of 0.06 and validation RMSE of 0.08. Most importantly, our model can be used for the challenging task of predicting differences in the UV-vis spectral signatures of regioisomers.

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2.

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic's evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (Mpro), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. This work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic "warhead" to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of Mpro with micromolar affinities (KI of 5.27 µM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

Accurate Prediction of Voltage of Battery Electrode Materials Using Attention-Based Graph Neural Networks.

Performing first-principles calculations to discover electrodes' properties in the large chemical space is a challenging task. While machine learning (ML) has been applied to effectively accelerate those discoveries, most of the applied methods ignore the materials' spatial information and only use predefined features: based only on chemical compositions. We propose two attention-based graph convolutional neural network techniques to learn the average voltage of electrodes. Our proposed methods, which combine both atomic composition and atomic coordinates in 3D-space, improve the accuracy in voltage prediction significantly when compared to composition-based ML models. The first model directly learns the chemical reaction of electrodes and metal ions to predict their average voltage, whereas the second model combines electrodes' ML predicted formation energy (Eform) to compute their average voltage. Our Eform-based model demonstrates improved accuracy in transferability from our subset of learned Li ions to Na ions. Moreover, we predicted the theoretical voltage of 10 NaxMPO4F (M = Ti, Cr, Fe, Cu, Mn, Co, and Ni) fluorophosphate battery frameworks, which are unavailable in the Material Project database. It could be shown that we can expect average voltages higher than 3.1 V from those Na battery frameworks except from the NaTiPO4F and TiPO4F pair of electrodes, which offer an average voltage of 1.32 V.

Artificial Intelligence for Autonomous Molecular Design: A Perspective.

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds.

The prerequisite of therapeutic drug design and discovery is to identify novel molecules and developing lead candidates with desired biophysical and biochemical properties. Deep generative models have demonstrated their ability to find such molecules by exploring a huge chemical space efficiently. An effective way to generate new molecules with desired target properties is by constraining the critical fucntional groups or the core scaffolds in the generation process. To this end, we developed a domain aware generative framework called 3D-Scaffold that takes 3D coordinates of the desired scaffold as an input and generates 3D coordinates of novel therapeutic candidates as an output while always preserving the desired scaffolds in generated structures. We demonstrated that our framework generates predominantly valid, unique, novel, and experimentally synthesizable molecules that have drug-like properties similar to the molecules in the training set. Using domain specific data sets, we generate covalent and noncovalent antiviral inhibitors targeting viral proteins. To measure the success of our framework in generating therapeutic candidates, generated structures were subjected to high throughput virtual screening via docking simulations, which shows favorable interaction against SARS-CoV-2 main protease (Mpro) and nonstructural protein endoribonuclease (NSP15) targets. Most importantly, our deep learning model performs well with relatively small 3D structural training data and quickly learns to generalize to new scaffolds, highlighting its potential application to other domains for generating target specific candidates.

Machine Learning Screening of Metal-Ion Battery Electrode Materials.

Rechargeable batteries provide crucial energy storage systems for renewable energy sources, as well as consumer electronics and electrical vehicles. There are a number of important parameters that determine the suitability of electrode materials for battery applications, such as the average voltage and the maximum specific capacity which contribute to the overall energy density. Another important performance criterion for battery electrode materials is their volume change upon charging and discharging, which contributes to determine the cyclability, Coulombic efficiency, and safety of a battery. In this work, we present deep neural network regression machine learning models (ML), trained on data obtained from the Materials Project database, for predicting average voltages and volume change upon charging and discharging of electrode materials for metal-ion batteries. Our models exhibit good performance as measured by the average mean absolute error obtained from a 10-fold cross-validation, as well as on independent test sets. We further assess the robustness of our ML models by investigating their screening potential beyond the training database. We produce Na-ion electrodes by systematically replacing Li-ions in the original database by Na-ions and, then, selecting a set of 22 electrodes that exhibit a good performance in energy density, as well as small volume variations upon charging and discharging, as predicted by the machine learning model. The ML predictions for these materials are then compared to quantum-mechanics based calculations. Our results reaffirm the significant role of machine learning techniques in the exploration of materials for battery applications.

Quantum Mechanical Methods Predict Accurate Thermodynamics of Biochemical Reactions.

Thermodynamics plays a crucial role in regulating the metabolic processes in all living organisms. Accurate determination of biochemical and biophysical properties is important to understand, analyze, and synthetically design such metabolic processes for engineered systems. In this work, we extensively performed first-principles quantum mechanical calculations to assess its accuracy in estimating free energy of biochemical reactions and developed automated quantum-chemistry (QC) pipeline (https://appdev.kbase.us/narrative/45710) for the prediction of thermodynamics parameters of biochemical reactions. We benchmark the QC methods based on density functional theory (DFT) against different basis sets, solvation models, pH, and exchange-correlation functionals using the known thermodynamic properties from the NIST database. Our results show that QC calculations when combined with simple calibration yield a mean absolute error in the range of 1.60-2.27 kcal/mol for different exchange-correlation functionals, which is comparable to the error in the experimental measurements. This accuracy over a diverse set of metabolic reactions is unprecedented and near the benchmark chemical accuracy of 1 kcal/mol that is usually desired from DFT calculations.

Revealing the Formation Energy-Exfoliation Energy-Structure Correlation of MAB Phases Using Machine Learning and DFT.

MAB phases became popular as ultrahigh-temperature materials with high damage tolerance and excellent electrical conductivity. MAB is used to exfoliate two-dimensional (2D) transition-metal borides (MBenes), which are promising materials for developing next-generation nanodevices. In this report, we explore the correlation between the formation energy, exfoliation energy, and structural factors of MAB phases with orthorhombic and hexagonal crystal symmetries using density functional theory (DFT) and machine learning. For this, we developed three different machine learning models based on the support vector machine, deep neural network, and random forest regressor to study the stability of the MAB phases by calculating their formation energies. Our support vector machine and deep neural network models are capable of predicting the formation energies with mean absolute errors less than 0.1 eV/atom. MAB phases with the chemical formulas, MAB, M2AB2, and M3AB4, where M = Nb, Mn, Ti, W, V, Sc, Cr, Hf, Mo, Zr, Ta, and Fe, and A = group III-A elements (Al, Ga, In and Tl), were investigated to find out the formation energy and their structure correlation. We demonstrated that the stability of a MAB phase for a given transition-metal decreases when the A element changes from Al to Tl. DFT revealed that M-A and B-A bond strength strongly correlates with the stability of MAB phases. In addition, the exfoliation possibility of 2D MBenes becomes higher when the A element changes from Al to Tl because of weakening of M-A and B-A bonds.

Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries.

Machine-learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work, we develop a tool ( http://se.cmich.edu/batteries ) based on ML models to predict voltages of electrode materials for metal-ion batteries. To this end, we use deep neural network, support vector machine, and kernel ridge regression as ML algorithms in combination with data taken from the Materials Project database, as well as feature vectors from properties of chemical compounds and elemental properties of their constituents. We show that our ML models have predictive capabilities for different reference test sets and, as an example, we utilize them to generate a voltage profile diagram and compare it to density functional theory calculations. In addition, using our models, we propose nearly 5000 candidate electrode materials for Na- and K-ion batteries. We also make available a web-accessible tool that, within a minute, can be used to estimate the voltage of any bulk electrode material for a number of metal ions. These results show that ML is a promising alternative for computationally demanding calculations as a first screening tool of novel materials for battery applications.

Shrinking Self-Interaction Errors with the Fermi-Löwdin Orbital Self-Interaction-Corrected Density Functional Approximation.

The self-interaction error (SIE) is one of the major drawbacks of practical exchange-correlation functionals for Kohn-Sham density functional theory. Despite this, the use of methods that explicitly remove SIE from approximate density functionals is scarce in the literature due to their relatively high computational cost and lack of consistent improvement over standard modern functionals. In this article we assess the performance of a novel approach recently proposed by Pederson, Ruzsinszky, and Perdew [ J. Chem. Phys. 2014, 140, 121103] for performing self-interaction free calculations in density functional theory based on Fermi orbitals. To this end, we employ test sets consisting of reaction energies that are considered particularly sensitive to SIE. We found that the parameter-free Fermi-Löwdin orbital self-interaction correction method combined with the standard local spin density approximation (LSDA) and Perdew-Burke-Ernzerhof (PBE) functionals gives a much better estimate of reaction energies compared to their parent LSDA and PBE functionals for most of the reactions in these two sets. They also perform on par with the global PBE0 and range-separated LC-ωPBE hybrids, which partially eliminate the SIE by including Hartree-Fock exchange. This shows the potential of the Fermi-Löwdin orbital self-interaction correction (FLOSIC) method for practical density functional calculations without SIE.

Fermi-Löwdin orbital self-interaction correction to magnetic exchange couplings.

We analyze the effect of removing self-interaction error on magnetic exchange couplings using the Fermi-Löwdin orbital self-interaction correction (FLOSIC) method in the framework of density functional theory (DFT). We compare magnetic exchange couplings obtained from self-interaction-free FLOSIC calculations with the local spin density approximation (LSDA) with several widely used DFT realizations and wave function based methods. To this end, we employ the linear H-He-H model system, six organic radical molecules, and [Cu2Cl6]2- as representatives of different types of magnetic interactions. We show that the simple self-interaction-free version of LSDA improves calculated couplings with respect to LSDA in all cases, even though the nature of the exchange interaction varies across the test set, and in most cases, it yields results comparable to modern hybrids and range-separated approximate functionals.

Local Noncollinear Spin Analysis.

In this work, we generalize the local spin analysis of Clark and Davidson [J. Chem. Phys. 2001 115 (16), 7382] for the partitioning of the expectation value of the molecular spin square operator, ⟨S2⟩, into atomic contributions, ⟨SA·SB⟩, to the noncollinear spin case in the framework of density functional theory (DFT). We derive the working equations, and we show applications to the analysis of the noncollinear spin solutions of typical spin-frustrated systems and to the calculation of magnetic exchange couplings. In the former case, we employ the triangular H3He3 test molecule and a Mn3 complex to show that the local spin analysis provides additional information that complements the standard one-particle spin population analysis. For the calculation of magnetic exchange couplings, JAB, we employ the local spin partitioning to extract ⟨SA·SB⟩ as a function of the interatomic spin orientation given by the angle θ. This, combined with the dependence of the electronic energy with θ, provides a methodology to extract JAB from DFT calculations that, in contrast to conventional energy differences based methods, does not require the use of ad hoc SA and SB values.

Magnetic Exchange Couplings in Heterodinuclear Complexes Based on Differential Local Spin Rotations.

We analyze the performance of a new method for the calculation of magnetic exchange coupling parameters for the particular case of heterodinuclear transition metals complexes of Cu, Ni, and V. This method is based on a generalized perturbative approach which uses differential local spin rotations via formal Lagrange multipiers (Phillips, J. J.; Peralta, J. E. J. Chem. Phys. 2013, 138, 174115). The reliability of the calculated couplings has been assessed by comparing with results from traditional energy differences with different density functional approximations and with experimental values. Our results show that this method to calculate magnetic exchange couplings can be reliably used for heteronuclear transition metal complexes, and at the same time, that it is independent from the different mapping schemes used in energy difference methods.

Hexagonal BC3: A Robust Electrode Material for Li, Na, and K Ion Batteries.

We have investigated the stability, maximum intercalation capacity, and voltage profile of alkali metal intercalated hexagonal BC3 (MxBC3), for 0 < x ≤ 2 and M = Li, Na, and K. Our calculations, based on dispersion-corrected density functional theory, show that these intercalation compounds are stable with respect to BC3 and their bulk metal counterparts. Moreover, we found that among all MxBC3 considered, the maximum stable capacity corresponds to an x value of 1.5, 1, and 1.5 for Li, Na, and K, respectively. These values are associated with large gravimetric capacities of 572 mA h/g for Na and 858 mA h/g for Li and K. Importantly, we show that metal intercalated hexagonal BC3 has the advantage of a small open-circuit voltage variation of approximately 0.49, 0.12, and 0.16 V for Li, Na, and K, respectively. Our results suggest that BC3 can become a robust alternative to graphitic electrodes in metal ion batteries, thus encouraging further experimental work.