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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 OSTI2024

Gradient Coding With Iterative Block Leverage Score Sampling

Gradient coding is a method for mitigating straggling servers in a centralized computing network that uses erasure-coding techniques to distributively carry out first-order optimization methods. Randomized numerical linear algebra uses randomization to develop improved algorithms for large-scale linear algebra computations. In this study, we propose a method for distributed optimization that combines gradient coding and randomized numerical linear algebra. The proposed method uses a randomized ℓ 2 -subspace embedding and a gradient coding technique to distribute blocks of data to the computational nodes of a centralized network, and at each iteration the central server only requires a small number of computations to obtain the steepest descent update. The novelty of our approach is that the data is replicated according to importance scores, called block leverage scores, in contrast to most gradient coding approaches that uniformly replicate the data blocks. Furthermore, we do not require a decoding step at each iteration, avoiding a bottleneck in previous gradient coding schemes. We show that our approach results in a valid ℓ 2 -subspace embedding, and that our resulting approximation converges to the optimal solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Leverage Score Sampling for Parametric PDEs (Final Technical Report)

This final technical report summarizes the accomplishments of work performed under DOE Office of Science Award DE-SC0022266, which is titled “Leverage Score Sampling for Parametric PDEs”. The goal of the project was to extend methods from Randomized Numerical Linear Algebra (RandNLA) to tackle central computational challenges in model order reduction and uncertainty quantification (UQ) for parametric partial differential equations (PDEs). In particular, we sought to use importance sampling methods originally developed for RandNLA to develop sample efficient active learning algorithms for approximating high-dimensional scalar functions, e.g. by polynomials, Gaussian process models, and simple neural networks. Such methods can be immediately applied to developing surrogate models or to approximating quantity of interest (QoI) surfaces. In the context of PDEs, each sample used for learning equates to the solution of the differential equation for a particular set of parameters, so sample efficiency translates to improved computational efficiency for a variety of downstream tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Identifying Important Ions and Positions in Mass Spectrometry Imaging Data Using CUR Matrix Decompositions

Mass spectrometry imaging enables label-free, high-resolution spatial mapping of the chemical composition of complex, biological samples. Typical experiments require selecting ions and/or positions from the images: ions for fragmentation studies to identify keystone compounds and positions for follow up validation measurements using microdissection or other orthogonal techniques. Unfortunately, with modern imaging machines, these must be selected from an overwhelming amount of raw data. Existing techniques to reduce the volume of data, the most popular of which are principle component analysis and non-negative matrix factorization, have the disadvantage that they return difficult-to-interpret linear combinations of actual data elements. In this work, we show that CX and CUR matrix decompositions can be used directly to address this selection need. CX and CUR matrix decompositions use empirical statistical leverage scores of the input data to provide provably good low-rank approximations of the measured data that are expressed in terms of actual ions and actual positions, as opposed to difficult-to-interpret eigenions and eigenpositions. We show that this leads to effective prioritization of information for both ions and positions. In particular, important ions can be found either by using the leverage scores as a ranking function and using a deterministic greedy selection algorithm or by using the leverage scores as an importance sampling distribution and using a random sampling algorithm; however, selection of important positions from the original matrix performed significantly better when they were chosen with the random sampling algorithm. Also, we show that 20 ions or 40 locations can be used to reconstruct the original matrix to a tolerance of 17% error for a widely studied image of brain lipids; and we provide a scalable implementation of this method that is applicable for analysis of the raw data where there are often more than a million rows and/or columns, which is larger than SVD-based low-rank approximation methods can handle. Lastly, these results introduce the concept of CX/CUR matrix factorizations to mass spectrometry imaging, describing their utility and illustrating principled algorithmic approaches to deal with the overwhelming amount of data generated by modern mass spectrometry imaging.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Randomized Algorithms for Symmetric Nonnegative Matrix Factorization

Symmetric Nonnegative Matrix Factorization (SymNMF) is a technique in data analysis and machine learning that approximates a matrix with a product of a nonnegative, low-rank matrix and it transpose. To design faster and more scalable algorithms for SymNMF we develop two randomized algorithms for its computation. The first method uses randomized matrix sketching to compute an initial low-rank approximation to the input matrix and proceeds to uses this as a low-rank input to rapidly compute a SymNMF. The second methods uses randomized leverage score sampling to approximately solve constrained least squares problems. Many successful methods for SymNMF rely on (approximately) solving sequences of constrained least squares problems. Here, we prove theoretically that leverage score sampling can approximately solve constrained least squares problems to e-accuracy. Finally we demonstrate both methods work in practice by applying them to graph clustering tasks on large real world data sets. These experiments show that our methods approximately maintain solution quality and achieve significant speed ups for both large dense and large sparse problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Computing Nonequilibrium Responses with Score-Shifted Stochastic Differential Equations

Using equilibrium fluctuations to understand the response of a physical system to an externally imposed perturbation is the basis for linear response theory, which is widely used to interpret experiments and shed light on microscopic dynamics. For nonequilibrium systems, perturbations cannot be interpreted simply by monitoring fluctuations in a conjugate observable and general response results rely on path ensemble averaging. Furthermore, these techniques do not apply to perturbations that affect the diffusion tensor in a stochastic system. Here, we introduce an “effective” physical process that represents the diffusion perturbed dynamics and enables accurate calculations of responses to a change in the diffusion. Interestingly, the effective dynamics contain an additional drift involving the instantaneous “score” of the system, and we leverage score matching algorithms to carry out nonequilibrium response calculations on systems for which the exact stationary distribution is unknown.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Subsampling of Parametric Models with Bifidelity Boosting

Least squares regression is a ubiquitous tool for building emulators (a.k.a. surrogate models) of problems across science and engineering for purposes such as design space exploration and uncertainty quantification. When the regression data are generated using an experimental design process (e.g., a quadrature grid) involving computationally expensive models, or when the data size is large, sketching techniques have shown promise at reducing the cost of the construction of the regression model while ensuring accuracy comparable to that of the full data. However, random sketching strategies, such as those based on leverage scores, lead to regression errors that are random and may exhibit large variability. To mitigate this issue, we present a novel boosting approach that leverages cheaper, lower-fidelity data of the problem at hand to identify the best sketch among a set of candidate sketches. This in turn specifies the sketch of the intended high-fidelity model and the associated data. We provide theoretical analyses of this bifidelity boosting (BFB) approach and discuss the conditions the low- and high-fidelity data must satisfy for a successful boosting. In doing so, we derive a bound on the residual norm of the BFB sketched solution relating it to its ideal, but computationally expensive, high-fidelity boosted counterpart. Finally, empirical results on both manufactured and PDE data corroborate the theoretical analyses and illustrate the efficacy of the BFB solution in reducing the regression error, as compared to the nonboosted solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Application of Banking Scoring and Rating for Coherent Risk Measures in Electricity Systems ABSCORES

This project developed a framework for asset and system risk management that can be incorporated into current electricity system operations to improve economic efficiency and establish an Electric Assets Risk Bureau. We leveraged scoring and ratings from banking and financial institutions alongside current optimization methods in dispatching power systems to help system operators and electricity markets schedule resources. This approach is based on the observation that there are major discrepancies between the power scheduled by a system operator and the actual power generated/consumed. These discrepancies—exacerbated by unplanned contingencies (e.g., natural disasters)—are caused by multiple factors, including the different financial, environmental and risk preferences of power producers, consumers, and aggregators. We developed a framework that counteracts two failures in electricity system operations: imperfect information and missing markets for products. The technical approach included five tasks. Tasks 1 and 2 supported the development of risk scores at the asset level with historical data collected for this project. Tasks 3, 4, and 5 incorporated scoring into decision-making at the system level. The proposed effort achieved PERFORM's Program Objectives because the proposed outputs and algorithms do not exist in the electricity industry and are an innovative approach to managing risk. Since the acknowledged need to better assess and act upon risk profiles for grid assets has not been met by the industry, this project will also impact ARPA-E's Mission Areas, including improving energy efficiency and giving the U.S. a technological lead in advanced energy technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

A Provably Accurate Randomized Sampling Algorithm for Logistic Regression

In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly exceeds the number of predictor variables, we present a simple, randomized sampling-based algorithm for logistic regression problem that guarantees high-quality approximations to both the estimated probabilities and the overall discrepancy of the model. Our analysis builds upon two simple structural conditions that boil down to randomized matrix multiplication, a fundamental and well-understood primitive of randomized numerical linear algebra. We analyze the properties of estimated probabilities of logistic regression when leverage scores are used to sample observations, and prove that accurate approximations can be achieved with a sample whose size is much smaller than the total number of observations. To further validate our theoretical findings, we conduct comprehensive empirical evaluations. Overall, our work sheds light on the potential of using randomized sampling approaches to efficiently approximate the estimated probabilities in logistic regression, offering a practical and computationally efficient solution for large-scale datasets.

Chowdhury, Agniva↗
DOE OSTI2024

Patch2Self2: Self-supervised Denoising on Coresets via Matrix Sketching

Diffusion MRI (dMRI) non-invasively maps brain white matter yet necessitates denoising due to low signal-to-noise ratios. Patch2Self (P2S) employing self-supervised techniques and regression on a Casorati matrix effectively denoises dMRI images and has become the new de-facto standard in this field. P2S however is resource intensive both in terms of running time and memory usage as it uses all voxels (n) from all-but-one held-in volumes (d-1) to learn a linear mapping Phi : \mathbb R ^ n x(d-1) \mapsto \mathbb R ^ n for denoising the held-out volume. The increasing size and dimensionality of higher resolution dMRI acquisitions can make P2S infeasible for large-scale analyses. This work exploits the redundancy imposed by P2S to alleviate its performance issues and inspect regions that influence the noise disproportionately. Specifically this study makes a three-fold contribution: (1) We present Patch2Self2 (P2S2) a method that uses matrix sketching to perform self-supervised denoising. By solving a sub-problem on a smaller sub-space so called coreset we show how P2S2 can yield a significant speedup in training time while using less memory. (2) We present a theoretical analysis of P2S2 focusing on determining the optimal sketch size through rank estimation a key step in achieving a balance between denoising accuracy and computational efficiency. (3) We show how the so-called statistical leverage scores can be used to interpret the denoising of dMRI data a process that was traditionally treated as a black-box. Experimental results on both simulated and real data affirm that P2S2 maintains denoising quality while significantly enhancing speed and memory efficiency achieved by training on a reduced data subset.

Fadnavis, Shreyas↗
DOE OSTI2020

Pushing Green: Leveraging Home Energy Score to Promote Deep-Energy Retrofits in Portland, Oregon

In the United States, 39% of total energy is consumed by the building sector, 20% of which is attributed to residential buildings. Older homes, built before 1992 when the U.S. Department of Energy’s (DOE) Building Energy Codes Program was established, represent approximately 68% of residential building stock in the country and often have significant air leakage, inadequate insulation and inefficient heating & cooling systems. These older homes have long been the elephant in the room: how can programs effectively increase efficiency in all of the homes in need? Developed by DOE, the Home Energy Score was established to provide homeowners, buyers, and renters a comparable score measuring a home's energy use. Much like the MPG rating of automobiles, the Home Energy Score provides an overview of a residential building’s energy efficiency. In 2016, the City of Portland passed an ordinance requiring all residential properties to receive a Home Energy Score before being listed for sale, a mandate that was implemented January 1, 2018. Since 2018, more than 22,000 homes throughout the Portland Metro area have received Home Energy Scores (amounting to over 10% of Portland's single-family homes). This paper explores the sample of Home Energy Scores throughout Portland, including at the neighborhood level, identifies trends in efficiency measures, describes how the data has been used to date and proposes ways program administrators can use the Home Energy Score to increase the impact of energy efficiency programs on a city-scale.

Antonopoulos, Chrissi A.↗
DOE OSTI2018

Policy, Permit, Perform: Using City Benchmarking Data and Building Construction Permit History to Identify Energy Performance Improvements

The City of Portland (City) and Multnomah County 2015 Climate Action Plan targets a 40 percent reduction in carbon emissions below 1990 levels by 2030. To reach this goal, the plan includes a key objective to reduce the total energy use of existing buildings by 25 percent. Buildings are responsible for one-half of carbon emissions in Portland, Oregon, U.S., and improving their performance is critical to achieving the City’s climate goals. As part of its climate action planning, the City passed an ordinance on April 22, 2015, requiring commercial buildings 20,000 ft2 and larger to benchmark and disclose annual energy performance metrics through ENERGY STAR® Portfolio Manager® (ESPM), including greenhouse gas emissions, building energy use intensity (EUI) and ENERGY STAR scores. Twenty-five cities in the U.S. have adopted similar policies requiring energy benchmarking. These policies are generating a treasure trove of data about the drivers of building performance, but practitioners are only beginning to link benchmarking data to other datasets to produce actionable insight into improving building performance. In 2016, the City received a financial assistance award from the U.S. Department of Energy (DOE) through the Cities Leading through Energy Analysis and Planning project (Award Number DE-0007737). Portland’s project evaluated the application of the DOE Building Energy Asset Score Tool and rating system developed by Pacific Northwest National Laboratory (PNNL) to commercial buildings that report to the City. The City used building construction permit history to develop Asset Scores for a set of office buildings that reported energy performance for 2015 – the first year the City mandated energy benchmarking and reporting for commercial buildings 50,000 ft2 and larger. The primary project objective was to link energy benchmarking data and commercial building permit data to analyze commercial buildings systems that present the best, specific opportunities to improve energy performance. Integrating these datasets from two City departments, the Bureau of Development Services and the Bureau of Planning and Sustainability, could expand Portland’s use of analytics to inform climate policy decisions and strategic targeting of financial incentives for building owners and managers who are best positioned to improve the performance of their buildings. The project began with the commercial benchmarking data received for 340 commercial buildings that submitted calendar year 2015 energy reports. The City retained Research Into Action and Energy 350 (RIA team) to evaluate this self-reported benchmarking data. Based on a survey of 53 building managers, the RIA team found that most respondents used the City’s compliance tools, including a How-to Guide and Help Desk, and found access to utility data to be straightforward. However, almost one-third of the respondents encountered challenges when estimating the gross floor area (GFA), and some experienced confusion regarding the input of property use types and details into ESPM. Based on the RIA team’s recommendations, the City refined its How-to Guide, created online answers to frequently asked questions, and hosted office hour sessions to help building managers accurately characterize their building’s physical characteristics and operations in ESPM. Concurrently with the RIA team’s evaluation, the City worked with PNNL to estimate DOE Asset Score Preview (Preview) scores for 181 of the 340 commercial buildings that submitted ESPM reports to the City. Asset Score Tool is a web-based tool developed by PNNL for DOE to evaluate and rate the as-built energy efficiency of a building. Asset Score Tool’s full analysis capability runs a detailed energy model based on building geometry, age, envelope, lighting and mechanical systems using standard operating assumptions to give the building a score, similar to a miles per gallon rating for a car. Preview, a simplified deployment of the full Asset Score Tool, provides a preliminary score range using only seven data points, six of which are readily available in the ESPM benchmarking data. Analysis of the combined Preview scores and the operational ESPM scores identified buildings with a high potential for energy improvements through either retro-commissioning and retuning of existing operations or capital investments into building systems. The City contracted Whole Building Solutions (WBS) to mine building permit history and develop full Asset Scores for a subset of 26 office buildings that were eligible for both ENERGY STAR scores and Asset Scores. WBS researched permits and building plans to identify mechanical, lighting and envelope systems and other building characteristics necessary to complete Asset Scores. This historical data was recorded using the web-based Asset Score Tool, which evaluates the as designed energy efficiency of existing buildings. Much of this City information was available online after 2010; however, WBS had to search through microfiche files and the City’s database to obtain permit and plan history prior to 2010. Although building geometry, mechanical system type and basic envelope information was consistently identifiable, WBS found permit data gaps that prevented complete and accurate Asset Scores for most of the office buildings. If the City decides to develop Asset Scores based on building permit information in the future, WBS recommended requiring lighting plans, control diagrams and as-built drawings that reflect how the buildings are actually constructed. In addition, WBS recommended uploading plans and permits from microfiche storage into a more comprehensive, searchable database consistent with the City’s current online permit information. To identify the actual lighting types, WBS and City staff conducted site visits of all 26 office buildings. As part of managing the flow of ESPM and Asset Score data for the project, Earth Advantage, a sub-recipient of the Cities-LEAP award, created a DOE Standard Energy Efficiency Data (SEED) (USDOE 2018) instance for the City and used Asset Score’s Application Programming Interface (API) (Asset Score 2018) capabilities to develop two new features in the SEED Platform™: (1) automated Asset Score Preview ranges calculated based on benchmarking data and the City’s estimate of number of building floors; and (2) storage of full Asset Score information. Leveraging the construction permit history and site visit information for the 26 office buildings, The Cadmus Group (Cadmus) analyzed benchmarking data and Asset Score information to identify opportunities and drivers to improve performance. Correlation and regression analysis of the combined dataset was completed to determine whether building operations or specific systems – mechanical, lighting or envelope – present the best opportunities to improve energy performance. The central outcome of this project was Cadmus’ detailed analysis of the richer dataset that was created by combining the City of Portland’s commercial building permit activity and building-specific energy performance information based on metrics from ESPM and Asset Score Tool. Some of their findings collaborate with well-known facts, such as buildings with less efficient lighting systems are good targets for efficiency improvements through lighting upgrades. This analysis also helped identify that individual heating, ventilation, and air conditioning (HVAC) equipment efficiency does not necessarily correlate with building performance. However, overall HVAC system performance, as measured with the Total System Performance Ratio (TSPR) metric, is a better indicator of building performance (Goel et al. 2014). As a metric, TSPR considers whole system performance that includes equipment efficiency and system controls, such as resets for static pressure and chilled water setpoints, which can be more effective and financially feasible options for improving building performance. Given the high cost associated with envelope retrofits, opportunities to upgrade envelope insulation are likely to be limited. However, buildings with single-pane and non-thermal break windows are good candidates for window replacements, such as upgrading to double-pane windows with thermal breaks. This research project determined that developing Asset Scores based on site visits is preferable to mining City building permit data. In addition, the use of benchmarking data to develop Asset Score Preview Scores is recommended as a first step in engaging building managers to develop full Asset Scores. Asset Score Tool outputs can help building managers quickly identify opportunities for improvement and then work with the utility incentive administrator and energy service providers to develop their retrofit plan. Where full Asset Scores are available, analysis using a quadrant matrix with ENERGY STAR scores is recommended to identify buildings that present the best opportunities for operational-behavioral improvements versus ones with opportunities for upgrades to physical building systems. This workflow and the tools will help cities, utility incentive administrators, building portfolio managers and energy service providers develop energy programs and strategies for screening and analysis of buildings with the greatest energy savings potential. This report describes the project approach and research results to inform future coordination of ESPM and Asset Score data, including the use of benchmarking data to generate Preview scores. Given better awareness of energy performance, building managers can make more informed decisions to reduce energy consumption and carbon emissions. With cities, states and jurisdictions adopting benchmarking and auditing ordinances, there are substantial amounts of data submitted to cities that could be used to inform investments in energy efficiency.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Online randomized interpolative decomposition with a posteriori error estimator for temporal PDE data reduction

Traditional low-rank approximation is a powerful tool for compressing large data matrices that arise in simulations of partial differential equations (PDEs), but suffers from high computational cost and requires several passes over the PDE data. The compressed data may also lack interpretability thus making it difficult to identify feature patterns from the original data. Here, to address these issues, we present an online randomized algorithm to compute the interpolative decomposition (ID) of large-scale data matrices in situ. Compared to previous randomized IDs that used the QR decomposition to determine the column basis, we adopt a streaming ridge leverage score-based column subset selection algorithm that dynamically selects proper basis columns from the data and thus avoids an extra pass over the data to compute the coefficient matrix of the ID. In particular, we adopt a single-pass error estimator based on the non-adaptive Hutch++ algorithm to provide real-time error approximation for determining the best coefficients. As a result, our approach only needs a single pass over the original data and thus is suitable for large and high-dimensional matrices stored outside of core memory or generated in PDE simulations. A strategy to improve the accuracy of the reconstructed data gradient, when desired, within the ID framework is also presented. We provide numerical experiments on turbulent channel flow and ignition simulations, and on the NSTX Gas Puff Image dataset, comparing our algorithm with the offline ID algorithm to demonstrate its utility in real-world applications.

Column subset selection↗
DOE OSTI2022

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

42 ENGINEERING↗
DOE OSTI2024

Advancing earth system model calibration: a diffusion-based method

Understanding of climate impact on ecosystems globally requires site-specific model calibration. Here we introduce a novel diffusion-based uncertainty quantification (DBUQ) method for efficient model calibration. DBUQ is a score-based diffusion model that leverages Monte Carlo simulation to estimate the score function and evaluates a simple neural network to quickly generate samples for approximating parameter posterior distributions. DBUQ is stable, efficient, and can effectively calibrate the model given diverse observations, thereby enabling rapid and site-specific model calibration on a global scale. This capability significantly advances Earth system modeling and our understanding of climate impacts on Earth systems. We demonstrate DBUQ's capability in E3SM land model calibration at the Missouri Ozark AmeriFlux forest site. Both synthetic and real-data applications indicate that DBUQ produces accurate parameter posterior distributions similar to those generated by Markov Chain Monte Carlo sampling but with 30X less computing time. This efficiency marks a significant stride in model calibration, paving the way for more effective and timely climate impact analyses.

Liu, Yanfang↗
DOE OSTI2017

Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets

De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from‐scratch construction of molecules is not limited to compounds in pre‐existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK's advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known X‐ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ∼13M drug‐like compounds (generic libraries), and (3) application to a challenging protein‐protein interface on the viral drug target HIVgp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets. © 2017 Wiley Periodicals, Inc.

Allen, William J.↗
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↗