Seismic attribute analysis of the Upper Morrow Sandstone and the Arbuckle Group from 3D-3C seismic data at Cutter Field, southwest Kansas

Arbuckle Group and Upper Morrow Sandstone reservoirs have pronounced economic and environmental importance to the state of Kansas because of their history of oil production and potential for CO2 storage. Characterizing and delineating these reservoirs with seismic methods is challenging for a number of geophysical reasons. This study investigates the accuracy with which analysis of post-stack 3D-3C seismic data can delineate Upper Morrow Sandstone reservoirs and predict Arbuckle Group rock properties at Cutter Field in southwest Kansas. P-P and P-SV seismic responses of the Upper Morrow Sandstone and Arbuckle Group are modeled using Zoeppritz’ equations and P-impedance inversion is performed. Seismic attributes are extracted at well locations and compared to models. The Upper Morrow Sandstone is below resolution of both the P-P and P-SV data. No significant correlation is evident between amplitudes or inverted P-impedance and Upper Morrow Sandstone thickness. Instantaneous frequency values of 43 ± 2 Hz are observed at well locations where Upper Morrow Sandstone thickness is greater than 5 m whereas values of 45 ± 6 Hz are observed at well locations where thickness is less than 5 m. The difference in the rms instantaneous frequency values is statistically significant at the 90% confidence interval. Well log data from the Arbuckle Group shows an approximate neutron porosity range of 3-13% and an inverse correlation between neutron porosity and P-impedance, significant at the 99.9% confidence interval with a standard error of regression of 2% porosity. Model-based P-impedance inversion and results and flow unit interpretation from well log data suggest that porosity and flow units within the Arbuckle Group can be approximated by a three-layer model. Investigators can draw upon the results of this study to guide seismic acquisition and interpretation practices in geologic settings analogous to Cutter Field

Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks

The process of screening molecules for desirable properties is a key step in several applications, ranging from drug discovery to material design. During the process of drug discovery specifically, protein-ligand docking, or chemical docking, is a standard in-silico scoring technique that estimates the binding affinity of molecules with a specific protein target. Recently, however, as the number of virtual molecules available to test has rapidly grown, these classical docking algorithms have created a significant computational bottleneck. We address this problem by introducing Deep Surrogate Docking (DSD), a framework that applies deep learning-based surrogate modeling to accelerate the docking process substantially. DSD can be interpreted as a formalism of several earlier surrogate prefiltering techniques, adding novel metrics and practical training practices. Specifically, we show that graph neural networks (GNNs) can serve as fast and accurate estimators of classical docking algorithms. Additionally, we introduce FiLMv2, a novel GNN architecture which we show outperforms existing state-of-the-art GNN architectures, attaining more accurate and stable performance by allowing the model to filter out irrelevant information from data more efficiently. Through extensive experimentation and analysis, we show that the DSD workflow combined with the FiLMv2 architecture provides a 9.496x speedup in molecule screening with a <3% recall error rate on an example docking task. Our open-source code is available at https://github.com/ryienh/graph-dock.Comment: Published as workshop paper at NeurIPS 2022 (AI for Science

1963: Abilene Christian College Bible Lectures - Full Text

THE CHRISTIAN AND MORALITY” Being the Abilene Christian College Annual Bible Lectures 1963 Price: $3.95 Published by ABILENE CHRISTIAN COLLEGE STUDENTS EXCHANGE ACC Station Abilene, Texa

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors

INTRODUCTION COVID-19 became a global pandemic partially as a result of the lack of easily deployable, broad-spectrum oral antivirals, which complicated its containment. Even endemically, and with effective vaccinations, it will continue to cause acute disease, death, and long-term sequelae globally unless there are accessible treatments. COVID-19 is not an isolated event but instead is the latest example of a viral pandemic threat to human health. Therefore, antiviral discovery and development should be a key pillar of pandemic preparedness efforts. RATIONALE One route to accelerate antiviral drug discovery is the establishment of open knowledge bases, the development of effective technology infrastructures, and the discovery of multiple potent antivirals suitable as starting points for the development of therapeutics. In this work, we report the results of the COVID Moonshot—a fully open science, crowdsourced, and structure-enabled drug discovery campaign—against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro). This collaboration may serve as a roadmap for the potential development of future antivirals. RESULTS On the basis of the results of a crystallographic fragment screen, we crowdsourced design ideas to progress from fragment to lead compounds. The crowdsourcing strategy yielded several key compounds along the optimization trajectory, including the starting compound of what became the primary lead series. Three additional chemically distinct lead series were also explored, spanning a diversity of chemotypes. The collaborative and highly automated nature of the COVID Moonshot Consortium resulted in >18,000 compound designs, >2400 synthesized compounds, >490 ligand-bound x-ray structures, >22,000 alchemical free-energy calculations, and >10,000 biochemical measurements—all of which were made publicly available in real time. The recently approved antiviral ensitrelvir was identified in part based on crystallographic data from the COVID Moonshot Consortium. This campaign led to the discovery of a potent [median inhibitory concentration (IC50) = 37 ± 2 nM] and differentiated (noncovalent and nonpeptidic) lead compound that also exhibited potent cellular activity, with a median effective concentration (EC50) of 64 nM in A549-ACE2-TMPRSS2 cells and 126 nM in HeLa-ACE2 cells without measurable cytotoxicity. Although the pharmacokinetics of the reported compound is not yet optimal for therapeutic development, it is a promising starting point for further antiviral discovery and development. CONCLUSION The success of the COVID Moonshot project in producing potent antivirals, building open knowledge bases, accelerating external discovery efforts, and functioning as a useful information-exchange hub is an example of the potential effectiveness of open science antiviral discovery programs. The open science, patent-free nature of the project enabled a large number of collaborators to provide in-kind support, including synthesis, assays, and in vitro and in vivo experiments. By making all data immediately available and ensuring that all compounds are purchasable from Enamine without the need for materials transfer agreements, we aim to accelerate research globally along parallel tracks. In the process, we generated a detailed map of the structural plasticity of Mpro, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data to spur further research into antivirals and discovery methodologies. We hope that this can serve as an alternative model for antiviral discovery and future pandemic preparedness. Further, the project also showcases the role of machine learning, computational chemistry, and high-throughput structural biology as force multipliers in drug design. Artificial intelligence and machine learning algorithms help accelerate chemical synthesis while balancing multiple competing molecular properties. The design-make-test-analyze cycle was accelerated by these algorithms combined with planetary-scale biomolecular simulations of protein-ligand interactions and rapid structure determination