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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 macrodomain of SARS-CoV-2

The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small-molecule inhibitors of Mac1 have great therapeutic potential, at the outset of the COVID-19 pandemic, there were no well-validated inhibitors for this protein nor, indeed, the macrodomain enzyme family, making this target a pharmacological orphan. Here, we report the structure-based discovery and development of several different chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high-resolution protein crystallography, and binding evaluation. Potent scaffolds were designed with in silico fragment linkage and by ultra-large library docking of over 450 million molecules. Both techniques leverage the computational exploration of tangible chemical space and are applicable to other pharmacological orphans. Overall, 160 ligands in 119 different scaffolds were discovered, and 153 Mac1-ligand complex crystal structures were determined, typically to 1 Å resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated conformational changes within the active site, and key inhibitor motifs that will template future drug development against Mac1.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

D–MOPH–25: diverse MOF–molecule pairs for Henry’s constants prediction

Computational methods like grand-canonical Monte Carlo simulations and machine learning (ML) have accelerated metal–organic frameworks (MOF) exploration but are typically limited to a narrow range of adsorbates due to data availability and force field constraints. In this study, we introduce a dataset of diverse MOF–molecule pairs for Henry’s constant prediction, D–MOPH–25, which systematically explores a diverse chemical space by combining 113 molecular adsorbates with over 5000 MOF structures through an active learning process. D–MOPH–25 constitutes the most diverse adsorbate dataset used in any ML study of molecular adsorption in MOFs to date. Our workflow builds a benchmark for predicting Henry’s constants at 300 K, leveraging conformal prediction for uncertainty quantification. Assessment through Shannon entropy and uniform manifold approximation and projection confirms the comprehensiveness of D–MOPH–25 while highlighting the importance of robust classification to filter out unphysical data points in regression tasks. Although future enhancements in model architecture and sampling criteria could improve predictive performance, our dataset already spans the target space using only 2.31% of total possibilities. This comprehensive dataset facilitates assessment of model generalizability across adsorbate species and can establish a foundation for high-throughput MOF screening and ML-driven separation processes.

active learning↗
DOE OSTI2023

mkite

mkite is a distributed computing platform for materials simulation. mkite is built with the server-client pattern, decoupling production databases from client runners. When used in combination with message brokers, mkite enables any available client to perform calculations without prior hardware specification on the server side. Furthermore, the software enables the creation of complex workflows with multiple inputs and branches, facilitating the exploration of combinatorial chemical spaces. The mkite suite provides recipes and tools to interact with package such as VASP, but is extensible to any other simulation package. Finally, mkite helps keeping the provenance of calculations in a SQL database. A complete description of the software is available at https://arxiv.org/abs/2301.08841.

Schwalbe Koda, Daniel↗
DOE OSTI2024

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

3D structural interaction fingerprints↗
DOE OSTI2024

Enhancing Docking Accuracy with PECAN2, a 3D Atomic Neural Network Trained without Co-Complex Crystal Structures

Decades of drug development research have explored a vast chemical space for highly active compounds. The exponential growth of virtual libraries enables easy access to billions of synthesizable molecules. Computational modeling, particularly molecular docking, utilizes physics-based calculations to prioritize molecules for synthesis and testing. Nevertheless, the molecular docking process often yields docking poses with favorable scores that prove to be inaccurate with experimental testing. To address these issues, several approaches using machine learning (ML) have been proposed to filter incorrect poses based on the crystal structures. However, most of the methods are limited by the availability of structure data. Here, we propose a new pose classification approach, PECAN2 (Pose Classification with 3D Atomic Network 2), without the need for crystal structures, based on a 3D atomic neural network with Point Cloud Network (PCN). The new approach uses the correlation between docking scores and experimental data to assign labels, instead of relying on the crystal structures. We validate the proposed classifier on multiple datasets including human mu, delta, and kappa opioid receptors and SARS-CoV-2 Mpro. Our results demonstrate that leveraging the correlation between docking scores and experimental data alone enhances molecular docking performance by filtering out false positives and false negatives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Uncertainty-driven dynamics for active learning of interatomic potentials

Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, the ML model provides an uncertainty estimate along with its prediction for each new atomic configuration. If the uncertainty estimate passes a certain threshold, then the configuration is included in the data set. Here we develop a strategy to more rapidly discover configurations that meaningfully augment the training data set. The approach, uncertainty-driven dynamics for active learning (UDD-AL), modifies the potential energy surface used in molecular dynamics simulations to favor regions of configuration space for which there is large model uncertainty. The performance of UDD-AL is demonstrated for two AL tasks: sampling the conformational space of glycine and sampling the promotion of proton transfer in acetylacetone. The method is shown to efficiently explore the chemically relevant configuration space, which may be inaccessible using regular dynamical sampling at target temperature conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions

Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, exhaustive in-distribution drug-target interaction testing across all pairs of human G protein-coupled receptors and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks that leverages network homophily and training-free graph neural networks with labels as features. We show that Chemical Space Neural Networks’ ability to make accurate predictions strongly correlates with network homophily. Thus, labels as features strongly increase a machine learning model’s capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 drug-target interactions, 539 compounds, 7 human G protein-coupled receptors) to discover novel drug-target interactions for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

Hansson, Frederik G↗
NASA NTRS2001

Chemical Engineering in Space

The aerospace industry has long been perceived as the domain of both physicists and mechanical engineers. This perception has endured even though the primary method of providing the thrust necessary to launch a rocket into space is chemical in nature. The chemical engineering and chemistry personnel behind the systems that provide access to space have labored in the shadows of the physicists and mechanical engineers. As exploration into the cosmos moves farther away from Earth, there is a very distinct need for new chemical processes to help provide the means for advanced space exploration. The state of the art in launch systems uses chemical propulsion systems, primarily liquid hydrogen and liquid oxygen, to provide the energy necessary to achieve orbit. As we move away from Earth, there are additional options for propulsion. Unfortunately, few of these options can compare to the speed or ease of use provided by the chemical propulsion agents. It is with great care and significant cost that gaseous compounds such as hydrogen and oxygen are liquefied and become dense enough to use for rocket fuel. These low-temperature liquids fall within a specialty area known as cryogenics. Cryogenics, the science and art of producing cold operating conditions for use on Earth, in orbit, or on some other nonterrestrial body, has become increasingly important to our ability to travel within our solar system. The production of cryogenic fuels and the long-term storage of these fluids are necessary for travel. As our explorations move farther away from Earth, we need to address how to produce the necessary fuels to make a round-trip. The cost and the size of these expeditions are extreme at best. If we take everything necessary for our survival for the round-trip, we invalidate any chance of travel in the near future. As with the early explorers on Earth, we need to harvest much of our energy and our life support from the celestial bodies. The in situ production of these energy sources is paramount to success. We are currently working on several processes to produce the propellants that would allow us to visit and explore the surface of Mars. The capabilities currently at our disposal for launching and delivering equipment to another planet or satellite dictate that the size and scale of any hardware must be extremely small. The miniaturization of the processes needed to prepare the in situ propellants and life support commodities is a real challenge. Chemical engineers are faced with the prospect of reproducing an entire production facility in miniature so the complex can be lifted into space and delivered to our destination. Another area that does not normally concern chemical engineers is the extreme physical aspects payloads are subjected to with the launch of a spacecraft. Extreme accelerations followed by the sudden loss of nearly all gravitational forces are well outside normal equipment design conditions. If the equipment cannot survive the overall trip, then it obviously will not be able to yield the needed products upon arrival. These launch constraints must be taken into account. Finally, we must consider both the effectiveness and efficiencies of the processes. A facility located on the Moon or Mars will not have an unlimited supply of power or other ancillary utilities. For a Mars expedition, the available electric power is severely limited. The design of both the processes and the equipment must be considered. With these constraints in mind, only the most efficient designs will be viable. Cryogenics, in situ resource utilization, miniaturization, launchability, and power/process efficiencies are only a few of the areas that chemical engineers provide support and expertise for the exploration of space.

Lobmeyer, Dennis A.↗
DOE OSTI2020

A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus

The multicomponent Ti alloys, specifically the -phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young’s modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nb- and Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young’s moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young’s modulus. Furthermore, we implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Application and assessment of deep learning for the generation of potential NMDA receptor antagonists

Uncompetitive antagonists of the N-methyl D-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable for both new medication development and preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. Overall, we present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds are still required.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

MISPR : an open-source package for high-throughput multiscale molecular simulations

Computational tools provide a unique opportunity to study and design optimal materials by enhancing our ability to comprehend the connections between their atomistic structure and functional properties. However, designing materials with tailored functionalities is complicated due to the necessity to integrate various computational-chemistry software (not necessarily compatible with one another), the heterogeneous nature of the generated data, and the need to explore vast chemical and parameter spaces. The latter is especially important to avoid bias in scattered data points-based models and derive statistical trends only accessible by systematic datasets. Here, we introduce a robust high-throughput multi-scale computational infrastructure coined MISPR (Materials Informatics for Structure–Property Relationships) that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT). By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. In this work, we describe fully automated DFT workflows implemented in MISPR to compute various properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions. The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure–property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Molecular dynamics simulations for glass transition temperature predictions of polyhydroxyalkanoate biopolymers

Polyhydroxyalkanoates (PHAs) represent an emerging class of biosynthetic and biodegradable polyesters that exhibit considerable potential to replace petroleum-based plastics towards a sustainable future. Despite the promise, general structure–property mappings within this class of polymers remain largely unexplored. An efficient exploration of this vast chemical space calls for the development and validation of predictive methods for accurate estimation of a diverse range of properties for PHA-based polymers. Towards this aim, we present and validate in this work the results of our molecular dynamics (MD) simulation based approach aimed at predicting glass transition temperatures (T g ) of PHA-based polymers. Since generally available and widely used polymer forcefields exhibit a relatively poor performance for T g predictions, we have developed a new forcefield by modifying the polymer consistent force field (PCFF) via refining a selected set of torsion potentials of the polymer backbone using accurate density functional theory (DFT) computations. After carefully assessing the dependence of critical simulation parameters, such as, polymer chain length, number of polymer chains, supercell size, and thermal quenching rate used in the simulation, the applicability and transferability of the modified PCFF (mPCFF) is demonstrated by directly comparing the computed T g predictions of various polymers with different chemistries, polymer side chain lengths and functional groups forming the polymer side chains against the respective experimentally measured values. Furthermore, the transport properties such as self-diffusion coefficient and viscosity are computationally determined and their well-known correlation with the target properties is demonstrated. Lastly, we have employed the developed approach to predict T g values for a number of yet-to-be-synthesized PHA-based polymers with a diverse set of functional groups in the polymer side chains. The results are further rationalized by correlating the predicted T g values with the inter-chain H-bond formation tendencies of the different side chain functional groups. This work represents an important first step towards computationally guided design of PHA-based functional polymers and opens up new directions for a systematic investigation of composition- and configuration-dependent structure–property relationships in more complex binary and ternary copolymer systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1985

Hydrogen-deficient atmospheres for cool carbon stars

Motivated by recent work which hints at a possible deficiency of hydrogen in non-Mira N-type carbon stars and to further explore the parameter space of chemical composition, computations have been made of a series of hydrogen-deficient models for carbon stars. For these models Teff = 3000 K, and log g = 0.0. Solar abundances are used for all elements except for carbon (which is enhanced to give C/O = 1.05), hydrogen, and helium. As the fractional abundance of hydrogen is decreased, being replaced by helium, the temperature-optical depth relation is affected only slightly, but the temperature-pressure relation is changed. The most striking change in the emergent flux is the decrease of the H(-) peak at 1.65 micron compared with the blackbody peak at 1.00 micron.

Johnson, H. R.↗
NASA NTRS1993

Chemical and luminosity evolution, and counts of galaxies in a merger model

A merger model is applied to the chemical and luminosity evolution of galaxies. Two aspects are focused on. The first is the problem of abundance ratios as a function of metallicity. The second is related to the luminosity evolution of galaxies. In relation to the former, we calculate the evolution of several chemical elements exploring a broad space of possible star formation rates, including those derived using phenomenological arguments from a multiple merger galaxy formation scenario. We are able to reproduce the observed plateau in the ratio of the abundances of oxygen to iron versus metallicity as a direct consequence of one of the merging SFR used; we have utilized a standard Type II supernovae nucleosynthesis scenario coupled with a reasonable binary model for Type Ia supernovae and its consequent nucleosynthetic yields. Following the consequent luminosity effects in a straightforward way enables the estimation of the evolution of bolometric luminosity. We have used our recently developed code for photometric evolution of galaxies to make a preliminary computation of the number-magnitude relationship, assuming a standard picture of galaxy evolution, in the B and K bands.

Colin, P.↗
NASA NTRS2008

NASA Propulsion Investments for Exploration and Science

The National Aeronautics and Space Administration (NASA) invests in chemical and electric propulsion systems to achieve future mission objectives for both human exploration and robotic science. Propulsion system requirements for human missions are derived from the exploration architecture being implemented in the Constellation Program. The Constellation Program first develops a system consisting of the Ares I launch vehicle and Orion spacecraft to access the Space Station, then builds on this initial system with the heavy-lift Ares V launch vehicle, Earth departure stage, and lunar module to enable missions to the lunar surface. A variety of chemical engines for all mission phases including primary propulsion, reaction control, abort, lunar ascent, and lunar descent are under development or are in early risk reduction to meet the specific requirements of the Ares I and V launch vehicles, Orion crew and service modules, and Altair lunar module. Exploration propulsion systems draw from Apollo, space shuttle, and commercial heritage and are applied across the Constellation architecture vehicles. Selection of these launch systems and engines is driven by numerous factors including development cost, existing infrastructure, operations cost, and reliability. Incorporation of green systems for sustained operations and extensibility into future systems is an additional consideration for system design. Science missions will directly benefit from the development of Constellation launch systems, and are making advancements in electric and chemical propulsion systems for challenging deep space, rendezvous, and sample return missions. Both Hall effect and ion electric propulsion systems are in development or qualification to address the range of NASA s Heliophysics, Planetary Science, and Astrophysics mission requirements. These address the spectrum of potential requirements from cost-capped missions to enabling challenging high delta-v, long-life missions. Additionally, a high specific impulse chemical engine is in development that will add additional capability to performance-demanding space science missions. In summary, the paper provides a survey of current NASA development and risk reduction propulsion investments for exploration and science.

Smith, Bryan K.↗
NASA NTRS2012

Development of the Packed Bed Reactor ISS Flight Experiment

Packed bed reactors are compact, require minimum power and maintenance to operate, and are highly reliable. These features make this technology a leading candidate as a potential unit operation in support of long duration human space exploration. On earth, this type of reactor accounts for approximately 80% of all the reactors used in the chemical process industry today. Development of this technology for space exploration is truly crosscutting with many other potential applications (e.g., in-situ chemical processing of planetary materials and transport of nutrients through soil). NASA is developing an ISS experiment to address this technology with particular focus on water reclamation and air revitalization. Earlier research and development efforts funded by NASA have resulted in two hydrodynamic models which require validation with appropriate instrumentation in an extended microgravity environment. The first model developed by Motil et al., (2003) is based on a modified Ergun equation. This model was demonstrated at moderate gas and liquid flow rates, but extension to the lower flow rates expected in many advanced life support systems must be validated. The other model, developed by Guo et al., (2004) is based on Darcy s (1856) law for two-phase flow. This model has been validated for a narrow range of flow parameters indirectly (without full instrumentation) and included test points where the flow was not fully developed. The flight experiment presented will be designed with removable test sections to test the hydrodynamic models. The experiment will provide flexibility to test additional beds with different types of packing in the future. One initial test bed is based on the VRA (Volatile Removal Assembly), a packed bed reactor currently on ISS whose behavior in micro-gravity is not fully understood. Improving the performance of this system through an accurate model will increase our ability to purify water in the space environment.

Patton, Martin O.↗
NASA NTRSDate not supplied

NASA’s Developments in Cryogenic Fluid Management Technology

NASA has been pursuing the development and demonstration of enabling and enhancing technologies for Cryogenic Fluid Management (CFM) in collaboration with the US industry. NASA’s vision for current and future space exploration requires high-performance chemical and nuclear propulsion solutions that utilize cryogens. The Agency will use cryogenic fluids for Earth-to-orbit transportation, human missions to the Moon and Mars, planetary exploration, and in-situ resource utilization (ISRU) production. All these architectures will require state-of-the-art long-duration storage on the surface and in-space transfer for refueling. This paper focuses on key CFM technology developments completed or ongoing by the Cryogenic Fluid Management Portfolio Office within NASA’s Space Technology Mission Directorate. The CFM Portfolio Office, partnered with US aerospace industry, has invested in CFM technology ground and flight demonstrations, focused on maturing CFM hardware and operations for Artemis and Moon to Mars activities. These demonstrations additionally provide NASA with data to inform numerical modeling activities for verification and validation of design tools and approaches for future missions. This paper includes discussion on remaining high-priority open CFM technology gaps for Mar-forward architectures and initiatives for closure.

Cryogenic Fluid Management↗
DOE OSTI2020

A Quick Route to Multiple Highly Potent SARS-CoV-2 Main Protease Inhibitors**

The COVID-19 pathogen, SARS-CoV-2, requires its main protease (SC2M Pro ) to digest two of its translated long polypeptides to form a number of mature proteins that are essential for viral replication and pathogenesis. Inhibition of this vital proteolytic process is effective in preventing the virus from replicating in infected cells and therefore provides a potential COVID-19 treatment option. Guided by previous medicinal chemistry studies about SARS-CoV-1 main protease (SC1M Pro ), we have designed and synthesized a series of SC2M Pro inhibitors that contain β-(S-2-oxopyrrolidin-3-yl)-alaninal (Opal) for the formation of a reversible covalent bond with the SC2M Pro active-site cysteine C145. All inhibitors display high potency with K i values at or below 100 nM. The most potent compound, MPI3, has as a K i value of 8.3 nM. Crystallographic analyses of SC2M Pro bound to seven inhibitors indicated both formation of a covalent bond with C145 and structural rearrangement from the apoenzyme to accommodate the inhibitors. Virus inhibition assays revealed that several inhibitors have high potency in inhibiting the SARS-CoV-2-induced cytopathogenic effect in both Vero E6 and A549/ACE2 cells. Two inhibitors, MPI5 and MPI8, completely prevented the SARS-CoV-2-induced cytopathogenic effect in Vero E6 cells at 2.5-5 μM and A549/ACE2 cells at 0.16-0.31 μM. Their virus inhibition potency is much higher than that of some existing molecules that are under preclinical and clinical investigations for the treatment of COVID-19. Our study indicates that there is a large chemical space that needs to be explored for the development of SC2M Pro inhibitors with ultra-high antiviral potency.

3C-like protease↗