Computational Bioinformatics Research Articles

A landscape of resistance gene analogs in sour cherry (Prunus cerasus L.)

ObjectiveThis research aims to analyze the presence and distribution of resistance genes in the avium and fruticosa subgenomes of Prunus cerasus through computational methods and bioinformatics tools.ResultsAnalysis of genome and transcriptome sequencing data revealed a total of 19,570 transcripts with at least one resistance gene domain in Prunus cerasus subgenome avium and 19,142 in Prunus cerasus subgenome fruticosa. Key findings include the identification of 804 “complete” resistance gene transcripts in Prunus cerasus subgenome avium and 817 in Prunus cerasus subgenome fruticosa, with distinct distributions of resistance gene classes observed between the subgenomes. Phylogenetic analysis showed clustering of resistance genes, and unique resistance proteins were identified in each subgenome. Functional annotation comparisons with Arabidopsis thaliana highlighted shared and unique resistance genes, emphasizing the complexity of disease resistance in cherry species. Additionally, a higher diversity of RLKs and RLPs was observed, with 504 transcripts identified and 18 showing similarity to known reference genes.

Dominant point-based sequential and parallel algorithms for the multiple sequential substring constrained-LCS problem

The Longest Common Subsequence (LCS) problem is a well-known and studied problem in computer science and bioinformatics. It consists in finding the longest subsequence that is common to two or more given sequences. In this paper, we address the problem of finding all LCS for the Sequential Substring Constrained-LCS (SSCLCS) problem, called the Multiple SSCLCS problem. To solve this problem, we first propose a dominant point-based sequential algorithm, designed on a new Leveled Direct Acyclic Graph (DAG) that gives the correct evaluation order of subproblems to avoid redundancy due to overlap. Depending on whether the constraints may overlap or not, it requires O ( S | Σ | K + 1 + r + n | Σ |) and O ( S | Σ | K + 1 + n | Σ |) time with \(O(Max\_level+n|\Sigma |) \) space. S is the number of partial SSCLCS in a node, K is the number of DAG levels, n is the length of sequences, r is the total length of constraints, \(Max\_level \) is the number of nodes in the largest level of the DAG, and | Σ | is the length of the alphabet. Then, we derive a coarse-grained multicomputer parallel solution requiring \(O\left(\frac{S|\Sigma |^{K+1}+r+n|\Sigma |}{p}\right) \) and \(O\left(\frac{S|\Sigma |^{K+1}+n|\Sigma |}{p}\right) \) execution time, \(O(Max\_level+n|\Sigma |) \) memory space and O ( K ) communication rounds. p is the number of processors. Experimental results showed that the parallel algorithm is respectively 14.43 × and 19.19 × faster than the sequential algorithm on 32 and 64 processors.

Inferring gene regulatory networks with graph convolutional network based on causal feature reconstruction

Inferring gene regulatory networks through deep learning and causal inference methods is a crucial task in the field of computational biology and bioinformatics. This study presents a novel approach that uses a Graph Convolutional Network (GCN) guided by causal information to infer Gene Regulatory Networks (GRN). The transfer entropy and reconstruction layer are utilized to achieve causal feature reconstruction, mitigating the information loss problem caused by multiple rounds of neighbor aggregation in GCN, resulting in a causal and integrated representation of node features. Separable features are extracted from gene expression data by the Gaussian-kernel Autoencoder to improve computational efficiency. Experimental results on the DREAM5 and the mDC dataset demonstrate that our method exhibits superior performance compared to existing algorithms, as indicated by the higher values of the AUPRC metrics. Furthermore, the incorporation of causal feature reconstruction enhances the inferred GRN, rendering them more reasonable, accurate, and reliable.

Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis

Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutting-edge model called ProtICNN-BiLSTM, which combines attention-based Improved Convolutional Neural Networks (ICNN) and Bidirectional Long Short-Term Memory (BiLSTM) units seamlessly. Our main goal is to improve the accuracy of protein sequence classification by carefully optimizing performance through Bayesian Optimisation. ProtICNN-BiLSTM combines the power of CNN and BiLSTM architectures to effectively capture local and global protein sequence dependencies. In the proposed model, the ICNN component uses convolutional operations to identify local patterns. Captures long-range associations by analyzing sequence data forward and backwards. In advanced biological studies, Bayesian Optimisation optimizes model hyperparameters for efficiency and robustness. The model was extensively confirmed with PDB-14,189 and other protein data. We found that ProtICNN-BiLSTM outperforms traditional categorization models. Bayesian Optimization’s fine-tuning and seamless integration of local and global sequence information make it effective. The precision of ProtICNN-BiLSTM improves comparative protein sequence categorization. The study improves computational bioinformatics for complex biological analysis. Good results from the ProtICNN-BiLSTM model improve protein sequence categorization. This powerful tool could improve medical and biological research. The breakthrough protein sequence classification model is ProtICNN-BiLSTM. Bayesian optimization, ICNN, and BiLSTM analyze biological data accurately.

MCNN_MC: Computational Prediction of Mitochondrial Carriers and Investigation of Bongkrekic Acid Toxicity Using Protein Language Models and Convolutional Neural Networks.

Mitochondrial carriers (MCs) are essential proteins that transport metabolites across mitochondrial membranes and play a critical role in cellular metabolism. ADP/ATP (adenosine diphosphate/adenosine triphosphate) is one of the most important carriers as it contributes to cellular energy production and is susceptible to the powerful toxin bongkrekic acid. This toxin has claimed several lives; for example, a recent foodborne outbreak in Taipei, Taiwan, has caused four deaths and sickened 30 people. The issue of bongkrekic acid poisoning has been a long-standing problem in Indonesia, with reports as early as 1895 detailing numerous deaths from contaminated coconut fermented cakes. In bioinformatics, significant advances have been made in understanding biological processes through computational methods; however, no established computational method has been developed for identifying mitochondrial carriers. We propose a computational bioinformatics approach for predicting MCs from a broader class of secondary active transporters with a focus on the ADP/ATP carrier and its interaction with bongkrekic acid. The proposed model combines protein language models (PLMs) with multiwindow scanning convolutional neural networks (mCNNs). While PLM embeddings capture contextual information within proteins, mCNN scans multiple windows to identify potential binding sites and extract local features. Our results show 96.66% sensitivity, 95.76% specificity, 96.12% accuracy, 91.83% Matthews correlation coefficient (MCC), 94.63% F1-Score, and 98.55% area under the curve (AUC). The results demonstrate the effectiveness of the proposed approach in predicting MCs and elucidating their functions, particularly in the context of bongkrekic acid toxicity. This study presents a valuable approach for identifying novel mitochondrial complexes, characterizing their functional roles, and understanding mitochondrial toxicology mechanisms. Our findings, that utilize computational methods to improve our understanding of cellular processes and drug-target interactions, contribute to the development of therapeutic strategies for mitochondrial disorders, reducing the devastating effects of bongkrekic acid poisoning.

Current Development of Data Resources and Bioinformatics Tools for Anticoronavirus Peptide.

Since December 2019, the emergence of severe acute respiratory syndrome coronavirus 2, which gave rise to coronavirus disease 2019 (COVID-19), has considerably impacted global health. The identification of effective anticoronavirus peptides (ACVPs) and the establishment of robust data storage methods are critical in the fight against COVID-19. Traditional wet-lab peptide discovery approaches are timeconsuming and labor-intensive. With advancements in computer technology and bioinformatics, machine learning has gained prominence in the extraction of functional peptides from extensive datasets. In this study, we comprehensively review data resources and predictors related to ACVPs published over the past two decades. In addition, we analyze the influence of various factors on model performance. We have reviewed nine ACVP-containing databases, which integrate detailed information on protein fragments effective against coronaviruses, providing crucial references for the development of antiviral drugs and vaccines. Additionally, we have assessed 15 peptide predictors for antiviral or specifically anticoronavirus activity. These predictors employ computational models to swiftly screen potential antiviral candidates, offering an efficient pathway for drug development. Our study provides conclusive results and insights into the performance of different computational methods, and sheds light on the future trajectory of bioinformatics tools for ACVPs. This work offers a representative overview of contributions to the field, with an emphasis on the crucial role of ACVPs in combating COVID-19.

Quantum computing in bioinformatics: a systematic review mapping.

The field of quantum computing (QC) is expanding, with efforts being made to apply it to areas previously covered by classical algorithms and methods. Bioinformatics is one such domain that is developing in terms of QC. This article offers a broad mapping review of methods and algorithms of QC in bioinformatics, marking the first of its kind. It presents an overview of the domain and aids researchers in identifying further research directions in the early stages of this field of knowledge. The work presented here shows the current state-of-the-art solutions, focuses on general future directions, and highlights the limitations of current methods. The gathered data includes a comprehensive list of identified methods along with descriptions, classifications, and elaborations of their advantages and disadvantages. Results are presented not just in a descriptive table but also in an aggregated and visual format.

Current clinical status of IC/BPS and what the future holds in basic & translational science

The present review summarizes the scientific content in the workshop “Current clinical status of IC/BPS and what the future holds in basic & translational science” at the International Continence Society (ICS) 2023, Toronto. In the workshop, clinicians and scientists from different disciplines and nationalities discussed the current clinical status of IC/BPS diagnostic and treatment. They defined the available trends in biomarker search and translational medicine. The recent contribution of computational science and bioinformatics, together with artificial intelligence and the recent improvements in the use of animal models were also explored.The search for diagnostic and predictive biomarkers for IC/BPS patients is important. The use of well-refined and characterized animal models and the use of bioinformatics and artificial intelligence will be useful in this search.

Beyond Meta-Omics: Functional Genomics in Future Marine Microbiome Research.

When President Bill Clinton and Francis Collins, then the director of the National Human Genome Research Institute, celebrated the near completion of the human genome sequence at the White House in the summer of 2000, it is unlikely that they or anyone else could have predicted the blossoming of meta-omics in the following two decades and their applications in modern human microbiome and environmental microbiome research. This transformation was enabled by the development of high-throughput sequencing technologies and sophisticated computational biology tools and bioinformatics software packages. Today, environmental meta-omics has undoubtedly revolutionized our understanding of ocean ecosystems, providing the genetic blueprint of oceanic microscopic organisms. In this review, I discuss the importance of functional genomics in future marine microbiome research and advocate a position for a gene-centric, bottom-up approach in modern oceanography. I propose that a synthesis of multidimensional approaches is required for a better understanding of the true functionality of the marine microbiome.

Maximum-scoring path sets on pangenome graphs of constant treewidth.

We generalize a problem of finding maximum-scoring segment sets, previously studied by Csűrös (IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2004, 1, 139-150), from sequences to graphs. Namely, given a vertex-weighted graph G and a non-negative startup penalty c, we can find a set of vertex-disjoint paths in G with maximum total score when each path's score is its vertices' total weight minus c. We call this new problem maximum-scoring path sets (MSPS). We present an algorithm that has a linear-time complexity for graphs with a constant treewidth. Generalization from sequences to graphs allows the algorithm to be used on pangenome graphs representing several related genomes and can be seen as a common abstraction for several biological problems on pangenomes, including searching for CpG islands, ChIP-seq data analysis, analysis of region enrichment for functional elements, or simple chaining problems.

Application of network pharmacology in synergistic action of Chinese herbal compounds.

Herbal medicines are frequently blended in the form of multi-drug combinations primarily based on the precept of medicinal compatibility, to achieve the purpose of treating diseases. However, due to the lack of appropriate techniques and the multi-component and multi-target nature of Chinese medicine compounding, it is tough to explain how the drugs interact with each other. As a rising discipline, cyber pharmacology has formed a new approach characterized by using holistic and systematic "network targets" via the cross-fertilization of computer technology, bioinformatics, and different multidisciplinary disciplines. It can broadly screen the active ingredients of traditional Chinese medicine, enhance the effective utilization of drugs, and elucidate the mechanism of drug action. We will overview the principles of Chinese medicine compounding and dispensing, the research methods of network pharmacology, and the software of network pharmacology in the lookup of compounded Chinese medicines, aiming to supply thoughts for the better application of network pharmacology in the research of Chinese medicines.

Computational study on the adsorption and mechanisms of the hydrogenation of N-Cbz-protected glycine, alanine, valine, and phenylalanine on palladium, Pd (1 0 0), surface

Materials Studio (MS) is powerful software (with various useful tools) that is designed for simulating and modeling of different molecules and materials in computational chemistry, cheminformatics, bioinformatics, and etc. Density functional theory (DFT), with periodic slab model, is one of the most applied theoretical approaches for understanding surface-adsorbate interaction strength and reactivity. In this study, DFT method have been used via Materials Studio software to study the adsorption (at T = 298.15 K, 308.15 K, and 318.15 K) and hydrogenation (at constant temperature T = 298.15 K) of four series of N-Cbz-protected amino acids (Cbz-aa's include N-Cbz-l-phenylalanine, Cbz-pheala, N-Cbz-l-valine, Cbz-val, N-Cbz-l-alanine, Cbz-ala, and N-Cbz-l-glycine, Cbz-gly) on palladium, Pd (1 0 0), surface. During calculations of this study, a cell with periodic boundary conditions was constructed and each Cbz-aa was located and studied in this cell. For calculations of the hydrogenation of Cbz-aa's on Pd (1 0 0) surface at constant temperature, Materials Studio QMERA (combined QM/MM methods) has been employed. For calculations of the adsorption of Cbz-aa's on Pd (1 0 0) surface at different temperatures, the Materials Studio Sorption module has been applied. In the adsorption processes, average adsorption isosteric heat (Qst) and average total energy or overall system energies (Etotal) were extracted. In the hydrogenation processes, the energy of reaction (ΔH) and energy barrier (Eb) were obtained. In this study, three different mechanisms (mechanism 1: M1, mechanism 2: M2, and mechanism 3: M3) were suggested for the hydrogenation of each Cbz-aa on Pd (1 0 0) surface. We suggested these mechanisms based on the sites (atoms) that are probable for adding two hydrogen atoms (from H2) as well as the strength of bonds that should be broken for adding two hydrogen atoms (from H2). For these three mechanisms, the energy of reaction (ΔH), transition state, and energy barrier (Eb) were calculated. The obtained values of ΔH show that mechanism 3 (M3) provides the most facile mechanism to perform hydrogenation of Cbz-aa's on Pd (1 0 0) surface. Further, the negative sign of ΔH for M3 shows that mechanism 3 is an exothermic reaction in the case of all our studied Cbz-aa's. Hydrogenation and adsorption data show that Qst very slowly decreases and Etotal very slowly increases when temperature increases. Also, molecular weight and stereochemistry of Cbz-aa's molecules are two important factors that affect the values of Qst, Etotal, Eb, and ΔH.

Therapeutic Intervention of Serine Protease Inhibitors against Hepatitis C Virus.

Hepatitis C virus (HCV) is a globally prevalent and hazardous disorder that is responsible for inducing several persistent and potentially fatal liver diseases. Current treatment strategies offer limited efficacy, often accompanied by severe and debilitating adverse effects. Consequently, there is an urgent and compelling need to develop novel therapeutic interventions that can provide maximum efficacy in combating HCV while minimizing the burden of adverse effects on patients. One promising target against HCV is the NS3-4A serine protease, a complex composed of two HCV-encoded proteins. This non-covalent heterodimer is crucial in the viral life cycle and has become a primary focus for therapeutic interventions. Although peginterferon, combined with ribavirin, is commonly employed for HCV treatment, its efficacy is hampered by significant adverse effects that can profoundly impact patients' quality of life. In recent years, the development of direct-acting antiviral agents (DAAs) has emerged as a breakthrough in HCV therapy. These agents exhibit remarkable potency against the virus and have demonstrated fewer adverse effects when combined with other DAAs. However, it is important to note that there is a potential for developing resistance to DAAs due to alterations in the amino acid position of the NS3-4A protease. This emphasizes the need for ongoing research to identify strategies that can minimize the emergence of resistance and ensure long-term effectiveness. While the combination of DAAs holds promise for HCV treatment, it is crucial to consider the possibility of drug-drug interactions. These interactions may occur when different DAAs are used concurrently, potentially compromising their therapeutic efficacy. Therefore, carefully evaluating and monitoring potential drug interactions are vital to optimize treatment outcomes. In the pursuit of novel therapeutic interventions for HCV, the field of computational biology and bioinformatics has emerged as a valuable tool. These advanced technologies and methodologies enable the development and design of new drugs and therapeutic agents that exhibit maximum efficacy, reduced risk of resistance, and minimal adverse effects. By leveraging computational approaches, researchers can efficiently screen and optimize potential candidates, accelerating the discovery and development of highly effective treatments for HCV, treatments.

Advanced computational approaches to understand protein aggregation.

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Antioxidant network-based signatures cluster glioblastoma into distinct redox-resistant phenotypes.

Aberrant reactive oxygen species (ROS) production is one of the hallmarks of cancer. During their growth and dissemination, cancer cells control redox signaling to support protumorigenic pathways. As a consequence, cancer cells become reliant on major antioxidant systems to maintain a balanced redox tone, while avoiding excessive oxidative stress and cell death. This concept appears especially relevant in the context of glioblastoma multiforme (GBM), the most aggressive form of brain tumor characterized by significant heterogeneity, which contributes to treatment resistance and tumor recurrence. From this viewpoint, this study aims to investigate whether gene regulatory networks can effectively capture the diverse redox states associated with the primary phenotypes of GBM. In this study, we utilized publicly available GBM datasets along with proprietary bulk sequencing data. Employing computational analysis and bioinformatics tools, we stratified GBM based on their antioxidant capacities and evaluated the distinctive functionalities and prognostic values of distinct transcriptional networks in silico. We established three distinct transcriptional co-expression networks and signatures (termed clusters C1, C2, and C3) with distinct antioxidant potential in GBM cancer cells. Functional analysis of each cluster revealed that C1 exhibits strong antioxidant properties, C2 is marked with a discrepant inflammatory trait and C3 was identified as the cluster with the weakest antioxidant capacity. Intriguingly, C2 exhibited a strong correlation with the highly aggressive mesenchymal subtype of GBM. Furthermore, this cluster holds substantial prognostic importance: patients with higher gene set variation analysis (GSVA) scores of the C2 signature exhibited adverse outcomes in overall and progression-free survival. In summary, we provide a set of transcriptional signatures that unveil the antioxidant potential of GBM, offering a promising prognostic application and a guide for therapeutic strategies in GBM therapy.

Haplotype-resolved assembly of diploid and polyploid genomes using quantum computing

Precision medicine's emphasis on individual genetic variants highlights the importance of haplotype-resolved assembly, a computational challenge in bioinformatics given its combinatorial nature. While classical algorithms have made strides in addressing this issue, the potential of quantum computing remains largely untapped. Here, we present the vehicle routing problem (VRP) assembler: an approach that transforms this task into a vehicle routing problem, an optimization formulation solvable on a quantum computer. We demonstrate its potential and feasibility through a proof of concept on short synthetic diploid and triploid genomes using a D-Wave quantum annealer. To tackle larger-scale assembly problems, we integrate the VRP assembler with Google's OR-Tools, achieving a haplotype-resolved local assembly across the human major histocompatibility complex (MHC) region. Our results show encouraging performance compared to Hifiasm with phasing accuracy approaching the theoretical limit, underscoring the promising future of quantum computing in bioinformatics.

Genetic analysis of scab disease resistance in common bean (Phaseolus vulgaris) varieties using GWAS and functional genomics approaches

IntroductionScab is a fungal disease of common beans caused by the pathogen Elsinoë phaseoli. The disease results in major economic losses on common beans, and there are efforts to develop integrated pest management strategies to control the disease. Modern computational biology and bioinformatics tools were utilized to identify scab disease resistance genes in the common bean by identification of genomic regions and genes associated with resistance to scab disease during natural infection in the field.MethodsA diverse set of common bean accessions were analyzed for genetic association with scab disease resistance using a Genome-Wide Association Study design of infected plants and non-infected plants (controls). A fixed and random model circulating probability unification model of these two covariates that considers a minor allele frequency threshold value of 0.03 were deployed during the analysis. Annotation of genes proteins with significant association values was conducted using a machine learning algorithm of support vector machine on prPred using python3 on Linux Ubuntu 18.04 computing platform with an accuracy of 0.935.ResultsCommon bean accessions tested showed varying phenotypes of susceptibility to scab disease. Out of 179 accessions, 16 and 163 accessions were observed to be resistant and susceptible to scab disease, respectively. Genomic analysis revealed a significant association on chromosome one SNP S1_6571566 where the protein-coding sequence had a resistant possibility of 55% and annotated to the Enhancer of Poly-comb like protein.ConclusionThe significant differences in the phenotypic variability for scab disease indicate wide genetic variability among the common bean accessions. The resistant gene associated with scab disease was successfully identified by GWAS analysis. The identified common bean accessions resistant to scab disease can be adopted into breeding programs as sources of resistance.

A review of computational studies and bioinformatics analysis of effective drug as an inhibitor against EGFR/VEGFR-2 kinases

A review of computational studies and bioinformatics analysis of effective drug as an inhibitor against EGFR/VEGFR-2 kinases

Identification of Key Genes Associated with Polycystic Ovarian Syndrome and Endometrial and Ovarian Cancer through Bioinformatics.

Polycystic ovary syndrome (PCOS), a common endocrine disorder, is linked to increased risks of endometrial cancer (EC) and ovarian cancer (OC). Our study utilises bioinformatics analysis to identify shared gene signatures and elucidate biological processes between EC and OC and PCOS. The objective of this research is to unveil the common molecular landscape shared by PCOS and EC and OC. An observational computational bioinformatics analysis. Gene expression profiles for PCOS (GSE199225), EC (GSE215413) and OC (GSE174670) were obtained from the Gene Expression Omnibus database. Hub genes were identified through functional enrichment analysis and protein-protein interaction. Drug identification analyses were employed to find drugs targeting the hub genes. Key hub genes linking PCOS and EC includes RECQL4, RAD54L, ATR, CHTF18, WDHD1, CDT1, PLK1, PKMYT1, RAD18 and RPL3; for PCOS and OC, they include HMOX1, TXNRD1, NQO1, GCLC, GSTP1, PRDX1, SOD1, GPX3, BOP1 and BYSL. Gene Ontology analysis revealed DNA metabolic process in PCOS and EC, while in PCOS and OC, it identified the removal of superoxide radicals. Kyoto Encyclopaedia of Genes and Genomes pathway analysis highlighted cell cycle in PCOS and EC and hepatocellular carcinoma in PCOS and OC. Potential drugs for PCOS and EC include quercetin, calcitriol and testosterone; for PCOS and OC, eugenol and 1-chloro-2,4-dinitrobenzene are identified. These findings offer insights into potential therapeutic targets and pathways linking PCOS with EC and OC, enhancing our understanding of the molecular mechanisms involved in these associations.

MapLE: Matching Molecular Analogues Promptly with Low Computational Resources by Multi-Metrics Evaluation (Student Abstract)

Matching molecular analogues is a computational chemistry and bioinformatics research issue which is used to identify molecules that are structurally or functionally similar to a target molecule. Recent studies on matching analogous molecules have predominantly concentrated on enhancing effectiveness, often sidelining computational efficiency, particularly in contexts of low computational resources. This oversight poses challenges in many real applications (e.g., drug discovery, catalyst generation and so forth). To tackle this issue, we propose a general strategy named MapLE, aiming to promptly match analogous molecules with low computational resources by multi-metrics evaluation. Experimental evaluation conducted on a public biomolecular dataset validates the excellent and efficient performance of the proposed strategy.