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DOE OSTI2022

Integration of physiologically relevant photosynthetic energy flows into whole genome models of light‐driven metabolism

SUMMARY Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome‐scale metabolic models capture species‐specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non‐photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non‐radiative energy dissipation processes, cross‐compartment electron shuttling, and non‐photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico . Overall, we present a methodology for incorporating a common, informative data type into computational models of light‐driven metabolism and show its utilization within the design–build–test–learn cycle for engineering of photosynthetic organisms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Evaluating causal‐based feature selection for fuel property prediction models

Abstract In‐silico screening of novel biofuel molecules based on chemical and fuel properties is a critical first step in the biofuel evaluation process due to the significant volumes of samples required for experimental testing, the destructive nature of engine tests, and the costs associated with bench‐scale synthesis of novel fuels. Predictive models are limited by training sets of few existing measurements, often containing similar classes of molecules that represent just a subset of the potential molecular fuel space. Software tools can be used to generate every possible molecular descriptor for use as input features, but most of these features are largely irrelevant and training models on datasets with higher dimensionality than size tends to yield poor predictive performance. Feature selection has been shown to improve machine learning models, but correlation‐based feature selection fails to provide scientific insight into the underlying mechanisms that determine structure–property relationships. The implementation of causal discovery in feature selection could potentially inform the biofuel design process while also improving model prediction accuracy and robustness to new data. In this study, we investigate the benefits causal‐based feature selection might have on both model performance and identification of key molecular substructures. We found that causal‐based feature selection performed on par with alternative filtration methods, and that a structural causal model provides valuable scientific insights into the relationships between molecular substructures and fuel properties.

Nguyen, Bernard↗

DOE OSTI2026

A meshing framework for digital twins for extrusion based additive manufacturing

Additive manufacturing (AM) allows for manufacturing of complex three-dimensional geometries not typically realizable with standard manufacturing practices. The internal microstructure of AM components has a significant impact on mechanical, vibrational, and shock properties and permits richer design space when this is controllable. Due to complex interactions of internal geometry of an extrusion-based AM component, it is common practice to assume homogeneous behavior or to perform characterization testing on specific toolpath configurations. To avoid testing or material waste, it is necessary to develop a consistently accurate numerical simulation framework with relevant boundary value problems that can handle the complicated geometry of internal material microstructure present in AM components. Herein, a framework is proposed to directly create computational meshes suitable for finite element analysis (FEA) of the fine-scale features generated from extrusion-based AM tool paths to maintain a strong process–structure–property-performance linkage. This mesh can be manually or automatically analyzed using standard FEA simulations such as quasi-static preloading or modal analysis. The framework allows an in-silico assessment of a target AM geometry where fine-scale features greatly impact quantities of design interest such as in soft elastomeric lattices where toolpath infill can greatly influence the self-contact of a structure in compression, which we use as a motivating exemplar. This approach greatly reduces both time and resource waste present in traditional build and test design cycles for non-intuitive design spaces, and acts as a tool for use in the production of a key component of a digital twin, a mesh suitable for finite element analysis. In conclusion, it also further allows for the exploration of toolpath infill to optimize component properties beyond simple linear properties such as density and stiffness.

Additive manufacturing↗

DOE OSTI2024

Physics-informed State-space Neural Networks for transport phenomena

This work introduces Physics -informed State -space neural network Models (PSMs), a novel solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous systems, particularly in transportdominated systems such as chemical, biomedical, and power plants. Traditional data -driven methods fall short due to a lack of physical constraints like mass conservation; PSMs address this issue by training deep neural networks with sensor data and physics -informing using components' Partial Differential Equations (PDEs), resulting in a physics -constrained, end -to -end differentiable forward dynamics model. Further, through two in silico experiments - a heated channel and a cooling system loop - we demonstrate that PSMs offer a more accurate approach than a purely data -driven model. In the former experiment, PSMs demonstrated significantly lower average root -mean -square errors across test datasets compared to a purely data -driven neural network, with reductions of 44 %, 48 %, and 94 % in predicting pressure, velocity, and temperature, respectively. Beyond accuracy, PSMs demonstrate a compelling multitask capability, making them highly versatile. In this work, we showcase two: supervisory control of a nonlinear system through a sequentially updated state -space representation and the proposal of a diagnostic algorithm using residuals from each of the PDEs. The former demonstrates PSMs' ability to handle constant and time -dependent constraints, while the latter illustrates their value in system diagnostics and fault detection.

42 ENGINEERING↗

DOE OSTI2023

Reverse engineering environmental metatranscriptomes clarifies best practices for eukaryotic assembly

Abstract Background Diverse communities of microbial eukaryotes in the global ocean provide a variety of essential ecosystem services, from primary production and carbon flow through trophic transfer to cooperation via symbioses. Increasingly, these communities are being understood through the lens of omics tools, which enable high-throughput processing of diverse communities. Metatranscriptomics offers an understanding of near real-time gene expression in microbial eukaryotic communities, providing a window into community metabolic activity. Results Here we present a workflow for eukaryotic metatranscriptome assembly, and validate the ability of the pipeline to recapitulate real and manufactured eukaryotic community-level expression data. We also include an open-source tool for simulating environmental metatranscriptomes for testing and validation purposes. We reanalyze previously published metatranscriptomic datasets using our metatranscriptome analysis approach. Conclusion We determined that a multi-assembler approach improves eukaryotic metatranscriptome assembly based on recapitulated taxonomic and functional annotations from an in-silico mock community. The systematic validation of metatranscriptome assembly and annotation methods provided here is a necessary step to assess the fidelity of our community composition measurements and functional content assignments from eukaryotic metatranscriptomes.

Krinos, Arianna I. (ORCID:0000000197678392)↗

DOE OSTI2025

A consensus mathematical model of vaccine-induced antibody dynamics for multiple vaccine platforms and pathogens

Introduction: Vaccine platforms used in successful, licensed vaccines have varied among pathogens. However, antibody level is still the main clinical correlate of protection in most approved vaccines. Decisions as to the best vaccine platform to pursue for a given pathogen may be informed through improved understanding of the process of antibody generation and its temporal dynamics, as well as the relationship between these processes and the type of vaccine. Methods: We have analyzed the dynamics of antibody generation for different vaccine platforms against diverse pathogens, and developed a consensus mathematical model that captures antibody dynamics across these diverse systems. Initially, the model was fitted to a rich dataset of antibody and immune cell concentrations in a SARS-CoV-2 vaccine experiment. We then used concepts from machine learning, such as transfer learning, to apply the same model to a variety of systems, involving different pathogens, vaccine platforms, and booster dose use/timing, fixing most parameter values relating to the dynamics of the immune system. Results: The model includes B cell proliferation and differentiation, as well as the generation of plasma cells, which secrete large amounts of antibody, and memory B cells. Overall, the model describes antibody generation in all systems tested well and shows that the main differences across platforms are related to the dynamics of antigen presentation. Discussion: This model can be used to predict antibody generation in pairs of vaccine platform/pathogen, allowing for the use of in silico results to narrow down experimental burden in vaccine development.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

PERCEPTIVE: an R shiny $\underline{p}$ipelin$\underline{e}$ for the p$\underline{r}$edi$\underline{c}$tion of $\underline{ep}$igenetic modula$\underline{t}$ors $\underline{i}$n no$\underline{v}$el sp$\underline{e}$cies

Epigenetic processes are central to regulating gene expression, genome stability, and metabolic function across the tree of life; yet, their roles remain underexplored in microalgae, especially as new species continue to be identified and characterized. This is likely due to the cumbersome nature and species-dependent attributes of epigenetic wet-lab methodologies, which preclude the rapid identification of epigenetic modifications and modulators. However, there is high conservation of epigenetic processes from budding yeast to humans; in many cases, one may infer how behavior and function are epigenetically regulated in novel species by identifying epigenetic modulators, or the proteins responsible for conferring epigenetic modifications. Here, to this end, we have developed a graphical software package, titled PERCEPTIVE (pipeline for the prediction of epigenetic modulators in novel species). This platform solely uses the genomic sequence of an algal species, and preexisting information from other model organisms, to predict the epigenetic modulators and associated modifications in algae. Predictions are presented to the user in a graphical interface, which provides literature-based interpretation of results, enabling users to quickly understand potential epigenetic processes in their algal species of interest and plan follow-up experiments. To test PERCEPTIVE, we predicted epigenetic modulators in several feedstock candidate algae species. To validate these predictions, wet-lab studies were performed, including mass spectrometry; these results underscore the high accuracy of PERCEPTIVE predictions. Overall, PERCEPTIVE represents a powerful in silico tool for the research and manipulation of algal species, which does not require a priori knowledge of epigenetics and is accessible to a broad set of investigators.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Fast Evaluation of Viral Emerging Risks (FEVER): A computational tool for biosurveillance, diagnostics, and mutation typing of emerging viral pathogens

Viral pathogens can rapidly evolve, adapt to novel hosts, and evade human immunity. The early detection of emerging viral pathogens through biosurveillance coupled with rapid and accurate diagnostics are required to mitigate global pandemics. However, RNA viruses can mutate rapidly, hampering biosurveillance and diagnostic efforts. Here, we present a novel computational approach called FEVER (Fast Evaluation of Viral Emerging Risks) to design assays that simultaneously accomplish: 1) broad-coverage biosurveillance of an entire group of viruses, 2) accurate diagnosis of an outbreak strain, and 3) mutation typing to detect variants of public health importance. We demonstrate the application of FEVER to generate assays to simultaneously 1) detect sarbecoviruses for biosurveillance; 2) diagnose infections specifically caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); and 3) perform rapid mutation typing of the D614G SARS-CoV-2 spike variant associated with increased pathogen transmissibility. These FEVER assays had a high in silico recall (predicted positive) up to 99.7% of 525,708 SARS-CoV-2 sequences analyzed and displayed sensitivities and specificities as high as 92.4% and 100% respectively when validated in 100 clinical samples. The D614G SARS-CoV-2 spike mutation PCR test was able to identify the single nucleotide identity at position 23,403 in the viral genome of 96.6% SARS-CoV-2 positive samples without the need for sequencing. This study demonstrates the utility of FEVER to design assays for biosurveillance, diagnostics, and mutation typing to rapidly detect, track, and mitigate future outbreaks and pandemics caused by emerging viruses.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

CFM-ID 4.0 – a web server for accurate MS-based metabolite identification

The CFM-ID 4.0 web server (https://cfmid.wishartlab.com) is an online tool for predicting, annotating and interpreting tandem mass (MS/MS) spectra of small molecules. It is specifically designed to assist researchers pursuing studies in metabolomics, exposomics and analytical chemistry. More specifically, CFM-ID 4.0 supports the: 1) prediction of electrospray ionization quadrupole time-of-flight tandem mass spectra (ESI-QTOF-MS/MS) for small molecules over multiple collision energies (10 eV, 20 eV, and 40 eV); 2) annotation of ESI-QTOF-MS/MS spectra given the structure of the compound; and 3) identification of a small molecule that generated a given ESI-QTOF-MS/MS spectrum at one or more collision energies. The CFM-ID 4.0 web server makes use of a substantially improved MS fragmentation algorithm, a much larger database of experimental and in silico predicted MS/MS spectra and improved scoring methods to offer more accurate MS/MS spectral prediction and MS/MS-based compound identification. Compared to earlier versions of CFM-ID, this new version has an MS/MS spectral prediction performance that is ~22% better and a compound identification accuracy that is ~35% better on a standard (CASMI 2016) testing dataset. CFM-ID 4.0 also features a neutral loss function that allows users to identify similar or substituent compounds where no match can be found using CFM-ID’s regular MS/MS-to-compound identification utility. Finally, the CFM-ID 4.0 web server now offers a much more refined user interface that is easier to use, supports molecular formula identification (from MS/MS data), provides more interactively viewable data (including proposed fragment ion structures) and displays MS mirror plots for comparing predicted with observed MS/MS spectra. These improvements should make CFM-ID 4.0 much more useful to the community and should make small molecule identification much easier, faster, and more accurate.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Genetics and Genomics of Pathogen Resistance in Switchgrass (Final Report)

This project was funded by DOE under Grant no. DE-SC0016108. Originally approved for the 2016-2019 period, two no-cost extensions were solicited and approved, which prolonged the lifespan through July 2021. This final report informs on the results obtained so far from the research implemented. The research hinged on integrating genomics (genomic selection, RNAseq, virus-plant interactions) with classical genetics (conventional breeding) to incorporate durable resistance to fungal (rust) and viral (mosaic) diseases in switchgrass (Panicum virgatum) populations being bred for bioenergy. Higher biomass yield, higher quality (low lignin content), and durable disease resistance are key features to make lignocellulosic switchgrass feedstocks economically competitive and sustainable. Genomic selection is being applied on three generations of a switchgrass population derived from crossing two ecotypes (Kanlow as lowland female and Summer as upland male) with differential performance in terms of biomass yield and quality, disease resistance, and winter survivability. Target populations were screened for rust and mosaic in field and/or lab and phenotyped for biomass yield and quality traits. Genetic analyses were applied across generations to capture the joint inheritance of the targeted traits and predict breeding values for parents and progeny with greater accuracy. Parental and a panel of different switchgrass populations were genotyped with the DArTseq technology to develop SNP (0, 1, 2) and in-silico (presence/absence) DArT markers. Rust inoculations techniques were developed and applied successfully on switchgrass. The original populations (Kanlow and Summer) were sequenced with RNAseq to capture the gene expression profiles across sequential time-points and appraise the basis of greater resistance in the Kanlow vs the Summer ecotype. Constructs of PMV and sPMV mosaic virus were assembled and tested first on proso millet to find the best protocol to use later on switchgrass. Results from the preliminary analyses indicate that 1) ample additive genetic variation is available for selection and improving this inter-ecotypic population for yield, quality, and disease traits, 2) significant gains are to be expected with the genetic correlations being favorable between yield and lignin content and between yield and disease ratings, 3) substantial differences exist in the genetic regions controlling rust resistance in the two ecotypes, 4) co-infection with PMV isolates from Nebraska and its satellite from Kansas elicit severe mosaic symptoms, and 5) two different genetic systems are responsible for imparting resistance to rust and virus in switchgrass.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals

Time courses of compound concentrations in plasma are used in chemical safety analysis to evaluate the relationship between external administered doses and internal tissue exposures. This type of experimental data is rarely available for the thousands of non-pharmaceutical chemicals to which people may potentially be unknowingly exposed but is necessary to properly assess the risk of such exposures. In vitro assays and in silico models are often used to craft an understanding of a chemical’s pharmacokinetics; however, the certainty of the quantitative application of these estimates for chemical safety evaluations cannot be determined without in vivo data for external validation. To address this need, we present a public database of chemical time-series concentration data from 567 studies in humans or test animals for 144 environmentally-relevant chemicals and their metabolites (187 analytes total). All major administration routes are incorporated, with concentrations measured in blood/plasma, tissues, and excreta. We also include calculated pharmacokinetic parameters for some studies, and a bibliography of additional source documents to support future extraction of time-series. In addition to pharmacokinetic model calibration and validation, these data may be used for analyses of diferential chemical distribution across chemicals, species, doses, or routes, and for meta-analyses on pharmacokinetic studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

An in silico method to assess antibody fragment polyreactivity

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally test our models’ performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the models allow us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its functional properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

microRNAs (miR 9, 124, 155 and 224) transdifferentiate mouse macrophages to neurons

Highlights: • miR 9, miR 124 and miR155 could reprogramme macrophages to mature neurons. • miR-induced neuronal reprogramming involves stem cell-like intermediate phenotype. • miR 9, miR 124 and miR155 could also reprogramme CD133+ stem cells to mature neurons. Development is an irreversible process of differentiating the undifferentiated cells to functional cells. Brain development involves generation of cells with varied phenotype and functions, which is limited during adulthood, stress, damage/degeneration. Cellular reprogramming makes differentiation reversible process with reprogramming somatic/stem cells to alternative fate with/without stem cells. Exogenously expressed transcription factors or small molecule inhibitors have driven reprogramming of stem/somatic cells to neurons providing alternative approach for pre-clinical/clinical testing and therapeutics. Here in, we report a novel approach of microRNA (miR)- induced trans-differentiation of macrophages (CD11b high) to induced neuronal cells (iNCs) (neuronal markershigh- Nestin, Nurr1, Map2, NSE, Tubb3 and Mash1) without exogenous use of transcription factors. miR 9, 124, 155 and 224 successfully transdifferentiated macrophages to neurons with transient stem cell-like phenotype. We report trans differentiation efficacy 18% and 21% with miR 124 and miR 155. in silico(String 10.0, miR gator, mESAdb, TargetScan 7.0) and experimental analysis indicate that the reprogramming involves alteration of pluripotencygenes like Oct4, Sox2, Klf4, Nanog and pluripotency miR, miR 302. iNCs also shifted to G0 phase indicating manipulation of cell cycle by these miRs. Further, CD133+ intermediate cells obtained during current protocol could be differentiated to iNCs using miRs. The syanpsin{sup +} neurons were functionally active and displayed intracellular Ca{sup +2} evoke on activation. miRs could also transdifferentiate bone marrow-derived macrophages and peripheral blood mononuclear cells to neuronal cells. The current protocol could be employed for direct in vivo reprogramming of macrophages to neurons without teratoma formation for transplantation and clinical studies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

A large-scale benchmarking of deterministic and stochastic derivative-free optimization algorithms

This presentation summarizes our work in the PrOMMiS project on benchmarking of data-driven optimization algorithms and their applications in self-driving laboratories. This work supports the broader project goal of accelerating the identification of promising separation methods and operating conditions for critical minerals separation processes. We present a systematic benchmarking study of 42 data-driven optimization algorithms on a broad collection of 502 test problems. The results identify BAM, GLCCLUSTER, and MULTIMIN as the most effective optimization solvers, with BAM showing the highest overall performance and solving more than 80% of the benchmark problems. The study also shows that no single solver consistently outperforms the others across all problem types, indicating that our future laboratory applications may benefit from using a small set of strong solvers rather than relying on a single method. The presentation also illustrates an in-silico chemical reactor case study showing that data-driven optimization methods can guide autonomous experimentation in a self-driving laboratory and identify optimal operating conditions within a small number of experiments. Overall, the results provide a basis for selecting efficient optimization methods and demonstrate the practical use of data-driven optimization in self-driving laboratory workflows.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Algorithm-guided experimentation for autonomous AI systems in self-driving laboratories

36 MATERIALS SCIENCE↗

DOE OSTI2020

Deep Learning Prediction of Interspecies Interactions from Self-organized Spatiotemporal Patterns of Co-evolving Organisms

Microorganisms colonizing natural habits such as soils co-evolve to form specific spatial patterns through interspecies interactions. These self-organized patterns are a key ecological phenotype, which provides critical information on their interaction mechanisms. However, conventional network inference techniques that analyze species population data in bulk have yet to be extended to account for such spatial heterogeneity. Here we proposed supervised deep learning as a new network inference tool for predicting interspecies interactions from spatiotemporal patterns of microbial evolution. Due to lack of biological imaging data that can be used for training deep learning networks, we used in silico data generated from high-fidelity agent-based models to determine model structure and parameters. Even though networks were trained under simple configurations where interaction coefficients are assumed to be spatially invariant, we demonstrated that the resulting model can be utilized to successfully predict spatial variation of interactions in more complex domains (i.e., configured with a context-dependent mixture of interaction coefficients) as well as in simple domains without further training. In the further test against real biological data obtained through imaging experiments of a binary consortium (Pseudomonas fluorescens and a mutant of Escherichia coli), our model also predicted the dramatic shifts in interactions of the two organisms across different environmental contexts. Through various successful demonstrations in this work, the combined use of the agent-based model and machine learning algorithm provides a means to use new type of data - microscopic images - for extracting microbial interactions, therefore presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Lee, Joon-Yong↗

DOE OSTI2023

Fine-scale evaluation of two standard 16S rRNA gene amplicon primer pairs for analysis of total prokaryotes and archaeal nitrifiers in differently managed soils

The advance of high-throughput molecular biology tools allows in-depth profiling of microbial communities in soils, which possess a high diversity of prokaryotic microorganisms. Amplicon-based sequencing of 16S rRNA genes is the most common approach to studying the richness and composition of soil prokaryotes. To reliably detect different taxonomic lineages of microorganisms in a single soil sample, an adequate pipeline including DNA isolation, primer selection, PCR amplification, library preparation, DNA sequencing, and bioinformatic post-processing is required. Besides DNA sequencing quality and depth, the selection of PCR primers and PCR amplification reactions arguably have the largest influence on the results. This study tested the performance and potential bias of two primer pairs, i.e., 515F (Parada)-806R (Apprill) and 515F (Parada)-926R (Quince) in the standard pipelines of 16S rRNA gene Illumina amplicon sequencing protocol developed by the Earth Microbiome Project (EMP), against shotgun metagenome-based 16S rRNA gene reads. The evaluation was conducted using five differently managed soils. We observed a higher richness of soil total prokaryotes by using reverse primer 806R compared to 926R, contradicting to in silico evaluation results. Both primer pairs revealed various degrees of taxon-specific bias compared to metagenome-derived 16S rRNA gene reads. Nonetheless, we found consistent patterns of microbial community variation associated with different land uses, irrespective of primers used. Total microbial communities, as well as ammonia oxidizing archaea (AOA), the predominant ammonia oxidizers in these soils, shifted along with increased soil pH due to agricultural management. In the unmanaged low pH plot abundance of AOA was dominated by the acid-tolerant NS-Gamma clade, whereas limed agricultural plots were dominated by neutral-alkaliphilic NS-Delta/NS-Alpha clades. This study stresses how primer selection influences community composition and highlights the importance of primer selection for comparative and integrative studies, and that conclusions must be drawn with caution if data from different sequencing pipelines are to be compared.

16S rRNA gene amplicon Illumina sequencing↗

DOE OSTI2025

Data-driven organic solubility prediction at the limit of aleatoric uncertainty

Abstract Small molecule solubility is a critically important property which affects the efficiency, environmental impact, and phase behavior of synthetic processes. Experimental determination of solubility is a time- and resource-intensive process and existing methods for in silico estimation of solubility are limited by their generality, speed, and accuracy. This work presents two models derived from the FASTPROP and CHEMPROP architectures and trained on BigSolDB which are capable of predicting solubility at arbitrary temperatures for a wide range of small molecules in organic solvent. Both extrapolate to unseen solutes 2–3 times more accurately than the current state-of-the-art model and we demonstrate that they are approaching the aleatoric limit (0.5–1$$\log S$$ log S ) of available test data, suggesting that further improvements in prediction accuracy require more accurate datasets. The FASTPROP-derived model (called FASTSOLV) and the CHEMPROP-based model are open source, freely accessible via a Python package and web interface, highly reproducible, and up to 2 orders of magnitude faster than current alternatives.

Science & Technology - Other Topics↗