Information extraction from historical well records using a large language model.

To reduce environmental risks and impacts from orphaned wells (abandoned oil and gas wells), it is essential to first locate and then plug these wells. Manual reading and digitizing of information from historical documents is not feasible, given the large number of wells. Here, we propose a new computational approach for rapidly and cost-effectively characterizing these wells.

Unlocking Solutions: Innovative Approaches to Identifying and Mitigating the Environmental Impacts of Undocumented Orphan Wells in the United States.

In the United States, hundreds of thousands of undocumented orphan wells have been abandoned, leaving the burden of managing environmental hazards to governmental agencies or the public. These wells, a result of over a century of fossil fuel extraction without adequate regulation, lack basic information like location and depth, emit greenhouse gases, and leak toxic substances into groundwater. For most of these wells, basic information such as well location and depth is unknown or unverified.

Aerial imagery dataset of lost oil wells.

Orphaned wells are wells for which the operator is unknown or insolvent. The location of hundreds of thousands of these wells remain unknown in the United States alone. Cost-effective techniques are essential to locate orphaned wells to address environmental problems.

G Protein-Coupled Receptor-Ligand Pose and Functional Class Prediction.

G protein-coupled receptor (GPCR) transmembrane protein family members play essential roles in physiology. Numerous pharmaceuticals target GPCRs, and many drug discovery programs utilize virtual screening (VS) against GPCR targets. Improvements in the accuracy of predicting new molecules that bind to and either activate or inhibit GPCR function would accelerate such drug discovery programs.

Progressive transfer learning for advancing machine learning-based reduced-order modeling.

To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs.

Discovery of Novel TLR7 Agonists as Systemic Agent for Combination With aPD1 for Use in Immuno-oncology.

We have designed and developed novel and selective TLR7 agonists that exhibited potent receptor activity in a cell-based reporter assay. , these agonists significantly induced secretion of cytokines IL-6, IL-1β, IL-10, TNFa, IFNa, and IP-10 in human and mouse whole blood. Pharmacokinetic and pharmacodynamic studies in mice showed a significant secretion of IFNα and TNFα cytokines.

Addressing quantum's "fine print" with efficient state preparation and information extraction for quantum algorithms and geologic fracture networks.

Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain in efficiency does not come without difficulty. Quantum algorithms require that problems satisfy not only algorithm-specific constraints, but also application-specific ones.

Quantum algorithms for geologic fracture networks.

Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglect information at small scales, an approach known as coarse-graining. For many practical applications, such as flow in porous, homogenous materials, coarse-graining offers a sufficiently-accurate approximation of the solution.

Physics-embedded inverse analysis with algorithmic differentiation for the earth's subsurface.

Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as algorithmic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.

Quantum computing and preconditioners for hydrological linear systems.

Modeling hydrological fracture networks is a hallmark challenge in computational earth sciences. Accurately predicting critical features of fracture systems, e.g.

Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning.

We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (DC-AE) has been shown to capture nonlinear solution manifolds but fails to perform adequately when linear subspace approaches such as proper orthogonal decomposition (POD) would be optimal. Besides, most DL-ROM models rely on convolutional layers, which might limit its application to only a structured mesh.

Enhancing high-fidelity nonlinear solver with reduced order model.

We propose the use of reduced order modeling (ROM) to reduce the computational cost and improve the convergence rate of nonlinear solvers of full order models (FOM) for solving partial differential equations. In this study, a novel ROM-assisted approach is developed to improve the computational efficiency of FOM nonlinear solvers by using ROM's prediction as an initial guess. We hypothesize that the nonlinear solver will take fewer steps to the converged solutions with an initial guess that is closer to the real solutions.

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management.

Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO[Formula: see text] sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. The heterogeneity typically requires high-fidelity physics-based models to make predictions on CO[Formula: see text] fate.

Identification and characterization of select oxysterols as ligands for GPR17.

Background And Purpose: G-protein coupled receptor 17 (GPR17) is an orphan receptor involved in the process of myelination, due to its ability to inhibit the maturation of oligodendrocyte progenitor cells (OPCs) into myelinating oligodendrocytes. Despite multiple claims that the biological ligand has been identified, it remains an orphan receptor.

Experimental Approach: Seventy-seven oxysterols were screened in a cell-free [ S]GTPγS binding assay using membranes from cells expressing GPR17.

Quantum annealing algorithms for Boolean tensor networks.

Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks.

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks.

Here we employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) for learning a forward and an inverse solution operator of partial differential equations (PDEs). We focus on steady-state solutions of coupled hydromechanical processes in heterogeneous porous media and present the parameterization of the spatially heterogeneous coefficients, which is exceedingly difficult using standard reduced-order modeling techniques. We show that our framework provides a speed-up of at least 2,000 times compared to a finite-element solver and achieves a relative root-mean-square error (r.

A machine learning framework for rapid forecasting and history matching in unconventional reservoirs.

We present a novel workflow for forecasting production in unconventional reservoirs using reduced-order models and machine-learning. Our physics-informed machine-learning workflow addresses the challenges to real-time reservoir management in unconventionals, namely the lack of data (i.e.

Discovery and Preclinical Pharmacology of an Oral Bromodomain and Extra-Terminal (BET) Inhibitor Using Scaffold-Hopping and Structure-Guided Drug Design.

Inhibition of the bromodomain and extra-terminal (BET) family of adaptor proteins is an attractive strategy for targeting transcriptional regulation of key oncogenes, such as c-MYC. Starting with the screening hit , a combination of structure-activity relationship and protein structure-guided drug design led to the discovery of a differently oriented carbazole with favorable binding to the tryptophan, proline, and phenylalanine (WPF) shelf conserved in the BET family. Identification of an additional lipophilic pocket and functional group optimization to optimize pharmacokinetic (PK) properties culminated in the discovery of (BMS-986158) with excellent potency in binding and functional assays.

Substituted diaryl ether compounds as retinoic acid-related orphan Receptor-γt (RORγt) agonists.

The discovery of a series of substituted diarylether compounds as retinoic acid related orphan receptor γt (RORγt) agonists is described. Compound 1 was identified from deck mining as a RORγt agonist. Hit-to-lead optimization led to the identification of lead compound 5, which possesses improved potency (10x).

Reverse annealing for nonnegative/binary matrix factorization.

It was recently shown that quantum annealing can be used as an effective, fast subroutine in certain types of matrix factorization algorithms. The quantum annealing algorithm performed best for quick, approximate answers, but performance rapidly plateaued. In this paper, we utilize reverse annealing instead of forward annealing in the quantum annealing subroutine for nonnegative/binary matrix factorization problems.

Transient flow modeling in fractured media using graphs.

We describe a method to simulate transient fluid flows in fractured media using an approach based on graph theory. Our approach builds on past work where the graph-based approach was successfully used to simulate steady-state fluid flows in fractured media. We find a mean computational speedup of the order of 1400 from an ensemble of a 100 discrete fracture networks in contrast to the O(10^{4}) speedup that was obtained for steady-state flows earlier.

Substituted benzyloxytricyclic compounds as retinoic acid-related orphan receptor gamma t (RORγt) agonists.

Substituted benzyloxy aryl compound 2 was identified as an RORγt agonist. Structure based drug design efforts resulted in a potent and selective tricyclic compound 19 which, when administered orally in an MC38 mouse tumor model, demonstrated a desired pharmacokinetic profile as well as a dose-dependent pharmacodynamic response. However, no perceptible efficacy was observed in this tumor model at the doses investigated.

Discovery of Pyridazinone and Pyrazolo[1,5-]pyridine Inhibitors of C-Terminal Src Kinase.

C-terminal Src kinase (CSK) functions as a negative regulator of T cell activation through inhibitory phosphorylation of LCK, so inhibitors of CSK are of interest as potential immuno-oncology agents. Screening of an internal kinase inhibitor collection identified pyridazinone lead , and a series of modifications led to optimized compound . Compound showed potent activity in biochemical and cellular assays and demonstrated the ability to increase T cell proliferation induced by T cell receptor signaling.

Nonnegative/Binary matrix factorization with a D-Wave quantum annealer.

D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest. Much of this interest has focused on the quantum behavior of D-Wave machines, and there have been few practical algorithms that use the D-Wave. Machine learning has been identified as an area where quantum annealing may be useful.

Nonnegative tensor factorization for contaminant source identification.

Unsupervised Machine Learning (ML) is becoming increasingly popular for solving various types of data analytics problems including feature extraction, blind source separation, exploratory analyses, model diagnostics, etc. Here, we have developed a new unsupervised ML method based on Nonnegative Tensor Factorization (NTF) for identification of the original groundwater types (including contaminant sources) present in geochemical mixtures observed in an aquifer. Frequently, groundwater types with different geochemical signatures are related to different background and/or contamination sources.