Guiding the choice of informatics software and tools for lipidomics research applications.

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

Bioimage informatics: Investing in software usability is essential.

In 2018, PLOS Biology announced CellProfiler 3.0, which has become one of the most used pieces of image analysis software in biology. The rapid adoption of this software speaks to the importance of user experience to disseminate new methods of bioimage informatics.

Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.

Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of RNA secondary structures; however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to RNA secondary structures, such as RNA-protein interactions, has also been enhanced, making a substantial contribution to the field of RNA biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of RNA-small molecule interactions for RNA-targeted drug discovery and in the design of RNA aptamers, where RNA serves as its own ligand. This review will highlight recent trends in the prediction of RNA secondary structure, RNA aptamers and RNA drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of RNA informatics.

Multidimensional Cross-Linking and Real-Time Informatics for Multiprotein Interaction Studies.

Chemical cross-linking combined with mass spectrometry is a technique used to study protein structures and identify protein complexes. Traditionally, chemical cross-linkers contain two reactive groups, allowing them to covalently bond a pair of proximal residues, either within a protein or between two proteins. The output of a cross-linking experiment is a list of interacting site pairs that provide structural constraints for modeling of new structures and complexes. Due to the binary reactive nature of cross-linking reagents, only pairs of interacting sites can be directly observed, and assembly of higher-order structures typically requires prior knowledge of complex composition or iterative docking to produce a putative model. Here, we describe a new tetrameric cross-linker bearing four amine-reactive groups, allowing it to covalently link up to four proteins simultaneously and a real-time instrument method to facilitate the identification of these tetrameric cross-links. We applied this new cross-linker to isolated mitochondria and identified a number of higher-order cross-links in various OXPHOS complexes and ATP synthase, demonstrating its utility in characterizing complex interfaces. We also show that higher-order cross-links can be used to effectively filter models of large protein assemblies generated by using Alphafold. Higher-dimensional cross-linking provides a new avenue for characterizing multiple protein interfaces, even in complex samples such as intact mitochondria.

Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis.

Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved 'high-coverage' and 'high-accuracy' glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

Biophysical informatics reveals distinctive phenotypic signatures and functional diversity of single-cell lineages.

MOTIVATION: In this work, we present an analytical method for quantifying both single-cell morphologies and cell network topologies of tumor cell populations and use it to predict 3D cell behavior. RESULTS: We utilized a supervised deep learning approach to perform instance segmentation on label-free live cell images across a wide range of cell densities. We measured cell shape properties and characterized network topologies for 136 single-cell clones derived from the YUMM1.7 and YUMMER1.7 mouse melanoma cell lines. Using an unsupervised clustering algorithm, we identified six distinct morphological subclasses. We further observed differences in tumor growth and invasion dynamics across subclasses in an in vitro 3D spheroid model. Compared to existing methods for quantifying 2D or 3D phenotype, our analytical method requires less time, needs no specialized equipment and is capable of much higher throughput, making it ideal for applications such as high-throughput drug screening and clinical diagnosis. AVAILABILITY AND IMPLEMENTATION: https://github.com/trevor-chan/Melanoma_NetworkMorphology. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

quickBAM: a parallelized BAM file access API for high-throughput sequence analysis informatics.

MOTIVATION: In time-critical clinical settings, such as precision medicine, genomic data needs to be processed as fast as possible to arrive at data-informed treatment decisions in a timely fashion. While sequencing throughput has dramatically increased over the past decade, bioinformatics analysis throughput has not been able to keep up with the pace of computer hardware improvement, and consequently has now turned into the primary bottleneck. Modern computer hardware today is capable of much higher performance than current genomic informatics algorithms can typically utilize, therefore presenting opportunities for significant improvement of performance. Accessing the raw sequencing data from BAM files, e.g. is a necessary and time-consuming step in nearly all sequence analysis tools, however existing programming libraries for BAM access do not take full advantage of the parallel input/output capabilities of storage devices. RESULTS: In an effort to stimulate the development of a new generation of faster sequence analysis tools, we developed quickBAM, a software library to accelerate sequencing data access by exploiting the parallelism in commodity storage hardware currently widely available. We demonstrate that analysis software ported to quickBAM consistently outperforms their current versions, in some cases finishing an analysis in under 3 min while the original version took 1.5 h, using the same storage solution. AVAILABILITY AND IMPLEMENTATION: Open source and freely available at https://gitlab.com/yiq/quickbam/, we envision that quickBAM will enable a new generation of high-performance informatics tools, either directly boosting their performance if they are currently data-access bottlenecked, or allow data-access to keep up with further optimizations in algorithms and compute techniques.

Formalizing Invertebrate Morphological Data: A Descriptive Model for Cuticle-Based Skeleto-Muscular Systems, an Ontology for Insect Anatomy, and their Potential Applications in Biodiversity Research and Informatics.

The spectacular radiation of insects has produced a stunning diversity of phenotypes. During the past 250 years, research on insect systematics has generated hundreds of terms for naming and comparing them. In its current form, this terminological diversity is presented in natural language and lacks formalization, which prohibits computer-assisted comparison using semantic web technologies. Here we propose a Model for Describing Cuticular Anatomical Structures (MoDCAS) which incorporates structural properties and positional relationships for standardized, consistent, and reproducible descriptions of arthropod phenotypes. We applied the MoDCAS framework in creating the ontology for the Anatomy of the Insect Skeleto-Muscular system (AISM). The AISM is the first general insect ontology that aims to cover all taxa by providing generalized, fully logical, and queryable, definitions for each term. It was built using the Ontology Development Kit (ODK), which maximizes interoperability with Uberon (Uberon multispecies anatomy ontology) and other basic ontologies, enhancing the integration of insect anatomy into the broader biological sciences. A template system for adding new terms, extending, and linking the AISM to additional anatomical, phenotypic, genetic, and chemical ontologies is also introduced. The AISM is proposed as the backbone for taxon-specific insect ontologies and has potential applications spanning systematic biology and biodiversity informatics, allowing users to: 1) use controlled vocabularies and create semiautomated computer-parsable insect morphological descriptions; 2) integrate insect morphology into broader fields of research, including ontology-informed phylogenetic methods, logical homology hypothesis testing, evo-devo studies, and genotype to phenotype mapping; and 3) automate the extraction of morphological data from the literature, enabling the generation of large-scale phenomic data, by facilitating the production and testing of informatic tools able to extract, link, annotate, and process morphological data. This descriptive model and its ontological applications will allow for clear and semantically interoperable integration of arthropod phenotypes in biodiversity studies.

Characterization and genome-informatic analysis of a novel lytic mendocina phage vB_PmeS_STP12 suitable for phage therapy pseudomonas or biocontrol.

BACKGROUND: A novel lytic bacteriophage (phage) was isolated with Pseudomonas mendocina strain STP12 (P. mendocina) from the untreated site of Sewage Treatment Plant of Lovely Professional University, India. P. mendocina is a Gram-negative, rod-shaped, aerobic bacterium belonging to the family Pseudomonadaceae and has been reported in fifteen (15) cases of economically important diseases worldwide. METHODS AND RESULTS: Here, a novel phage specifically infecting and killing P. mendocina strain STP12 was isolated from sewage sample using enrichment, spot test and double agar overlay (DAOL) method and was designated as vB_PmeS_STP12. The phage vB-PmeS-STP12 was viable at wide range of pH and temperature ranging from 4 to10 and - 20 to 70 °C respectively. Host range and efficiency of plating (EOP) analysis indicated that phage vB-PmeS-STP12 was capable of infecting and killing P. mendocina strain STP6 with EOP of 0.34. Phage vB_PmeS_STP12 was found to have a significant bacterial reduction (p < 0.005) at all the doses administered, particularly at optimal MOI of 1 PFU/CFU, compared to the control. Morphological analysis using high resolution transmission electron microscopy (HR-TEM) revealed an icosahedral capsid of ~ 55 nm in diameter on average with a short, non-contractile tail. The genome of vB_PmeS_STP12 is a linear, dsDNA containing 36,212 bp in size with a GC content of 58.87% harbouring 46 open reading frames (ORFs). The 46 predicted ORFs encode proteins with functional information categorized as lysis, replication, packaging, regulation, assembly, infection, immune, and hypothetical. However, the genome of vB_PmeS_STP12 appeared to be devoid of tRNAs, integrase gene, toxins genes, virulence factors, antimicrobial resistance genes (ARGs) and CRISPR arrays. The blast analysis with phylogeny revealed that vB_PmeS_STP12 is genetically similar to Pseudomonas phage PMBT14, Pseudomonas phage Almagne and Serratia phage Serbin with a highest identity of 74.00%, 74.93% and 59.48% respectively. CONCLUSIONS: Taken together, characterization, morphological analysis and genome-informatics indicated that vB_PmeS_STP12 is podovirus morphotype belonging to the class Caudoviticetes, family Zobellviridae which appeared to be devoid of integrase gene, ARGs, CRISPR arrays, virulence factors and toxins genes, exhibiting stability and infectivity at wide range of pH (4 to10) and temperature (-20 to 70 °C), thereby making vB_PmeS_STP12 suitable for phage therapy or biocontrol. Based on the bibliometric analysis and data availability with respect to sequences deposited in GenBank, this is the first report of a phage infecting Pseudomonas mendocina.

Informatics and Computational Approaches for the Discovery and Optimization of Natural Product-Inspired Inhibitors of the SARS-CoV-2 2'-O-Methyltransferase.

The urgent need for new classes of orally available, safe, and effective antivirals─covering a breadth of emerging viruses─is evidenced by the loss of life and economic challenges created by the HIV-1 and SARS-CoV-2 pandemics. As frontline interventions, small-molecule antivirals can be deployed prophylactically or postinfection to control the initial spread of outbreaks by reducing transmissibility and symptom severity. Natural products have an impressive track record of success as prototypic antivirals and continue to provide new drugs through synthesis, medicinal chemistry, and optimization decades after discovery. Here, we demonstrate an approach using computational analysis typically used for rational drug design to identify and develop natural product-inspired antivirals. This was done with the goal of identifying natural product prototypes to aid the effort of progressing toward safe, effective, and affordable broad-spectrum inhibitors of Betacoronavirus replication by targeting the highly conserved RNA 2'-O-methyltransferase (2'-O-MTase). Machaeriols RS-1 (7) and RS-2 (8) were identified using a previously outlined informatics approach to first screen for natural product prototypes, followed by in silico-guided synthesis. Both molecules are based on a rare natural product group. The machaeriols (3-6), isolated from the genus Machaerium, endemic to Amazonia, inhibited the SARS-CoV-2 2'-O-MTase more potently than the positive control, Sinefungin (2), and in silico modeling suggests distinct molecular interactions. This report highlights the potential of computationally driven screening to leverage natural product libraries and improve the efficiency of isolation or synthetic analog development.

Public Health Informatics and the Perioperative Physician: Looking to the Future.

The role of informatics in public health has increased over the past few decades, and the coronavirus disease 2019 (COVID-19) pandemic has underscored the critical importance of aggregated, multicenter, high-quality, near-real-time data to inform decision-making by physicians, hospital systems, and governments. Given the impact of the pandemic on perioperative and critical care services (eg, elective procedure delays; information sharing related to interventions in critically ill patients; regional bed-management under crisis conditions), anesthesiologists must recognize and advocate for improved informatic frameworks in their local environments. Most anesthesiologists receive little formal training in public health informatics (PHI) during clinical residency or through continuing medical education. The COVID-19 pandemic demonstrated that this knowledge gap represents a missed opportunity for our specialty to participate in informatics-related, public health-oriented clinical care and policy decision-making. This article briefly outlines the background of PHI, its relevance to perioperative care, and conceives intersections with PHI that could evolve over the next quarter century.

Integrative Research of Neuropharmacology and Informatics Pharmacology for Mental Disorder.

Mental illness poses a huge social burden, accounting for approximately 14% of all deaths. Depression, a major component of mental illness, affects approximately 300 million people worldwide, mainly in developed countries, and is not only a major social burden but also a cause of suicide. The social burden of depression is estimated to increase further in developing countries, and overcoming it is a pressing issue for all countries, including Japan. Although clinical evidence has demonstrated the efficacy of serotonergic neurotransmission enhancers in the treatment of depression, the full picture of their therapeutic effects has not yet been fully elucidated. In this review, we show that the hyperactivity of serotonin neurons, especially those in the dorsal raphe nucleus, is commonly induced by various antidepressants within a period corresponding to the onset of their clinical efficacy. We established quantitative prediction methods for pharmacological activity using only chemical structures to translate the biological understanding of mental disorders, including major depressive disorders, into clinically effective therapeutics. Our method exhibited better performance than the previously reported methods of quantitative prediction, while targeting a larger number of proteins. Our article suggests the importance of integrative neuropharmacology and informatics-based pharmacology studies to understand the biological basis of mental disorders and facilitate drug development for these disorders.

Whole-cycle management of women with epilepsy of child-bearing age: ontology construction and application.

BACKGROUND: The effective management of epilepsy in women of child-bearing age necessitates a concerted effort from multidisciplinary teams. Nevertheless, there exists an inadequacy in the seamless exchange of knowledge among healthcare providers within this context. Consequently, it is imperative to enhance the availability of informatics resources and the development of decision support tools to address this issue comprehensively. MATERIALS AND METHODS: The development of the Women with Epilepsy of Child-Bearing Age Ontology (WWECA) adhered to established ontology construction principles. The ontology's scope and universal terminology were initially established by the development team and subsequently subjected to external evaluation through a rapid Delphi consensus exercise involving domain experts. Additional entities and attribute annotation data were sourced from authoritative guideline documents and specialized terminology databases within the respective field. Furthermore, the ontology has played a pivotal role in steering the creation of an online question-and-answer system, which is actively employed and assessed by a diverse group of multidisciplinary healthcare providers. RESULTS: WWECA successfully integrated a total of 609 entities encompassing various facets related to the diagnosis and medication for women of child-bearing age afflicted with epilepsy. The ontology exhibited a maximum depth of 8 within its hierarchical structure. Each of these entities featured three fundamental attributes, namely Chinese labels, definitions, and synonyms. The evaluation of WWECA involved 35 experts from 10 different hospitals across China, resulting in a favorable consensus among the experts. Furthermore, the ontology-driven online question and answer system underwent evaluation by a panel of 10 experts, including neurologists, obstetricians, and gynecologists. This evaluation yielded an average rating of 4.2, signifying a positive reception and endorsement of the system's utility and effectiveness. CONCLUSIONS: Our ontology and the associated online question and answer system hold the potential to serve as a scalable assistant for healthcare providers engaged in the management of women with epilepsy (WWE). In the future, this developmental framework has the potential for broader application in the context of long-term management of more intricate chronic health conditions.

Bridging glycoinformatics and cheminformatics: integration efforts between GlyCosmos and PubChem.

The GlyCosmos Glycoscience Portal (https://glycosmos.org) and PubChem (https://pubchem.ncbi.nlm.nih.gov/) are major portals for glycoscience and chemistry, respectively. GlyCosmos is a portal for glycan-related repositories, including GlyTouCan, GlycoPOST, and UniCarb-DR, as well as for glycan-related data resources that have been integrated from a variety of 'omics databases. Glycogenes, glycoproteins, lectins, pathways, and disease information related to glycans are accessible from GlyCosmos. PubChem, on the other hand, is a chemistry-based portal at the National Center for Biotechnology Information. PubChem provides information not only on chemicals, but also genes, proteins, pathways, as well as patents, bioassays, and more, from hundreds of data resources from around the world. In this work, these 2 portals have made substantial efforts to integrate their complementary data to allow users to cross between these 2 domains. In addition to glycan structures, key information, such as glycan-related genes, relevant diseases, glycoproteins, and pathways, was integrated and cross-linked with one another. The interfaces were designed to enable users to easily find, access, download, and reuse data of interest across these resources. Use cases are described illustrating and highlighting the type of content that can be investigated. In total, these integrations provide life science researchers improved awareness and enhanced access to glycan-related information.

Metabolomics Peak Analysis Computational Tool (MPACT): An Advanced Informatics Tool for Metabolomics and Data Visualization of Molecules from Complex Biological Samples.

Untargeted metabolomics is a powerful tool for investigating chemistry of complex biological systems, but its utility is compromised by the presence of uninformative features and the limited efficiency of currently available prioritization tools. More effective filtering and prioritization tools are required to address the challenges of large untargeted metabolomics datasets. Here, we introduce Metabolomics Peak Analysis Computational Tool (MPACT), a new mass spectrometry data analysis platform employing filtering based on multiple modalities, statistical techniques incorporating multilevel replication, and interactive data visualization. We demonstrate application of MPACT to uncover hidden effects of the rare earth element cerium on tunicate-associated bacterium Streptomyces sp. PTY087I2, culminating in characterization of two thiolated compounds including a new cysteine derivative, granaticin C, and granaticin D, recently described as mycothiogranaticin A. While we demonstrate application of MPACT to microbial natural products discovery using an elicitation approach, the platform should be readily adaptable to investigation of multipartite interactions, biomarker detection, small molecules in the environment, and a wide range of other complex sample types.

Pharmacoinformatics and molecular dynamics simulation-based phytochemical screening of neem plant (Azadiractha indica) against human cancer by targeting MCM7 protein.

Minichromosome maintenance complex component 7 (MCM7) belongs to the minichromosome maintenance family that is important for the initiation of eukaryotic DNA replication. Overexpression of the MCM7 protein is relative to cellular proliferation and responsible for aggressive malignancy in various cancers. Mechanistically, inhibition of MCM7 significantly reduces the cellular proliferation associated with cancer. To date, no effective small molecular candidate has been identified that can block the progression of cancer induced by the MCM7 protein. Therefore, the study has been designed to identify small molecular-like natural drug candidates against aggressive malignancy associated with various cancers by targeting MCM7 protein. To identify potential compounds against the targeted protein a comprehensive in silico drug design including molecular docking, ADME (Absorption, Distribution, Metabolism and Excretion), toxicity, and molecular dynamics (MD) simulation approaches has been applied. Seventy phytochemicals isolated from the neem tree (Azadiractha indica) were retrieved and screened against MCM7 protein by using the molecular docking simulation method, where the top four compounds have been chosen for further evaluation based on their binding affinities. Analysis of ADME and toxicity properties reveals the efficacy and safety of the selected four compounds. To validate the stability of the protein-ligand complex structure MD simulations approach has also been performed to the protein-ligand complex structure, which confirmed the stability of the selected three compounds including CAS ID:105377-74-0, CID:12308716 and CID:10505484 to the binding site of the protein. In the study, a comprehensive data screening process has performed based on the docking, ADMET properties, and MD simulation approaches, which found a good value of the selected four compounds against the targeted MCM7 protein and indicates as a promising and effective human anticancer agent.

I2b2-etl: Python application for importing electronic health data into the informatics for integrating biology and the bedside platform.

MOTIVATION: The i2b2 platform is used at major academic health institutions and research consortia for querying for electronic health data. However, a major obstacle for wider utilization of the platform is the complexity of data loading that entails a steep curve of learning the platform's complex data schemas. To address this problem, we have developed the i2b2-etl package that simplifies the data loading process, which will facilitate wider deployment and utilization of the platform. RESULTS: We have implemented i2b2-etl as a Python application that imports ontology and patient data using simplified input file schemas and provides inbuilt record number de-identification and data validation. We describe a real-world deployment of i2b2-etl for a population-management initiative at MassGeneral Brigham. AVAILABILITY AND IMPLEMENTATION: i2b2-etl is a free, open-source application implemented in Python available under the Mozilla 2 license. The application can be downloaded as compiled docker images. A live demo is available at https://i2b2clinical.org/demo-i2b2etl/ (username: demo, password: Etl@2021). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BigSMARTS: A Topologically Aware Query Language and Substructure Search Algorithm for Polymer Chemical Structures.

Molecular search is important in chemistry, biology, and informatics for identifying molecular structures within large data sets, improving knowledge discovery and innovation, and making chemical data FAIR (findable, accessible, interoperable, reusable). Search algorithms for polymers are significantly less developed than those for small molecules because polymer search relies on searching by polymer name, which can be challenging because polymer naming is overly broad (i.e., polyethylene), complicated for complex chemical structures, and often does not correspond to official IUPAC conventions. Chemical structure search in polymers is limited to substructures, such as monomers, without awareness of connectivity or topology. This work introduces a novel query language and graph traversal search algorithm for polymers that provides the first search method able to fully capture all of the chemical structures present in polymers. The BigSMARTS query language, an extension of the small-molecule SMARTS language, allows users to write queries that localize monomer and functional group searches to different parts of the polymer, like the middle block of a triblock, the side chain of a graft, and the backbone of a repeat unit. The substructure search algorithm is based on the traversal of graph representations of the generating functions for the stochastic graphs of polymers. Operationally, the algorithm first identifies cycles representing the monomers and then the end groups and finally performs a depth-first search to match entire subgraphs. To validate the algorithm, hundreds of queries were searched against hundreds of target chemistries and topologies from the literature, with approximately 440,000 query-target pairs. This tool provides a detailed algorithm that can be implemented in search engines to provide search results with full matching of the monomer connectivity and polymer topology.

Data Sharing in Chemistry: Lessons Learned and a Case for Mandating Structured Reaction Data.

The past decade has seen a number of impressive developments in predictive chemistry and reaction informatics driven by machine learning applications to computer-aided synthesis planning. While many of these developments have been made even with relatively small, bespoke data sets, in order to advance the role of AI in the field at scale, there must be significant improvements in the reporting of reaction data. Currently, the majority of publicly available data is reported in an unstructured format and heavily imbalanced toward high-yielding reactions, which influences the types of models that can be successfully trained. In this Perspective, we analyze several data curation and sharing initiatives that have seen success in chemistry and molecular biology. We discuss several factors that have contributed to their success and how we can take lessons from these case studies and apply them to reaction data. Finally, we spotlight the Open Reaction Database and summarize key actions the community can take toward making reaction data more findable, accessible, interoperable, and reusable (FAIR), including the use of mandates from funding agencies and publishers.

The discovery of the natural compound Boeravinone-C as a potential antiobesity drug candidate targeting pancreatic lipase using chemo-informatics-based approaches.

Obesity is a metabolic disorder distinguished by excess fat deposition in fatty tissues. Pancreatic lipase is one of the promising drug targets for treating obesity due to its critical role in the hydrolysis of triglycerides into mono-glycerides and free fatty acids. Due to unsatisfactory results and severe side effects of the current drugs available for treating obesity, there is an urgent need to identify novel therapeutic options. Boerhaavia diffusa is one of the widely known species of flowering plant commonly known as Punamava. Extracts from Punamava plants have been widely used in treating countless ailments in traditional medicine. Recently, multiple reports demonstrated the potential antiobesity activity of B. diffusa plant extracts. In this scenario, we have evaluated numerous reported B. diffusa against pancreatic lipase drug targets to identify which reported phytochemicals to have the most promising potential to act as an inhibitor for pancreatic lipase using computational approaches. All the twenty-four phytochemicals from Boerhaavia diffusa were identified as significantly strong binders with a range of binding energies between -6.0 to -8.0 Kcal/mol inside the pancreatic lipase active binding site. On the other hand, we calculated 2D Quantitative Structure-Activity Relationship (QSAR) molecular descriptor properties adhered to Lipinski's rule of five. Between twenty-four phytochemicals evaluated, Boeravinone-C, with a range binding energy of -8.0 Kcal/mol, was discovered as the best lead-like molecule, compared to marketed Orlistat, which has shown -5.6 Kcal/mol of binding energy. Conclusively, Boeravinone-C from B. diffusa extract showed promising inhibitory potential against pancreatic lipase worth further evaluation.