GenerRNA: A generative pre-trained language model for de novo RNA design.

The design of RNA plays a crucial role in developing RNA vaccines, nucleic acid therapeutics, and innovative biotechnological tools. However, existing techniques frequently lack versatility across various tasks and are dependent on pre-defined secondary structure or other prior knowledge. To address these limitations, we introduce GenerRNA, a Transformer-based model inspired by the success of large language models (LLMs) in protein and molecule generation.

Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications.

Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the researches in quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR). With the growing number of ensemble learning models such as random forest, the effectiveness of QSAR/QSPR will be limited by the machine's inability to interpret the predictions to researchers.

Improvement of the Structure Generator DAECS with Respect to Structural Diversity.

The development of novel organic compounds with desired properties is time consuming and costly. Thus, the quantitative structure-property relationship (QSPR) model is used widely for efficiently discovering compounds with the desired properties. Novel structures can be generated from a variety of input structures in silico by structure generators.

Metabolic disassembler for understanding and predicting the biosynthetic units of natural products.

Background: Natural products are the source of various functional materials such as medicines, and understanding their biosynthetic pathways can provide information that is helpful for their effective production through the synthetic biology approach. A number of studies have aimed to predict biosynthetic pathways from their chemical structures in a retrosynthesis manner; however, sometimes the calculation finishes without reaching the starting material from the target molecule. In order to address this problem, the method to find suitable starting materials is required.

Allylic hydroxylation of triterpenoids by a plant cytochrome P450 triggers key chemical transformations that produce a variety of bitter compounds.

Cucurbitacins are highly oxygenated triterpenoids characteristic of plants in the family Cucurbitaceae and responsible for the bitter taste of these plants. Fruits of bitter melon () contain various cucurbitacins possessing an unusual ether bridge between C5 and C19, not observed in other Cucurbitaceae members. Using a combination of next-generation sequencing and RNA-Seq analysis and gene-to-gene co-expression analysis with the ConfeitoGUIplus software, we identified three P450 genes, , , and , expected to be involved in cucurbitacin biosynthesis.

Novel Electrotopological Atomic Descriptors for the Prediction of Xenobiotic Cytochrome P450 Reactions.

Cytochrome P450 (CYP) is an enzyme family that plays a crucial role in metabolism, mainly metabolizing xenobiotics to produce non-toxic structures, however, some metabolized products can cause hepatotoxicity. Hence, predicting the structures of CYP products is an important task in designing non-hepatotoxic drugs. Here, we have developed novel atomic descriptors to predict the sites of metabolism (SoM) in CYP substrates.

Network-based characterization of drug-protein interaction signatures with a space-efficient approach.

Background: Characterization of drug-protein interaction networks with biological features has recently become challenging in recent pharmaceutical science toward a better understanding of polypharmacology.

Results: We present a novel method for systematic analyses of the underlying features characteristic of drug-protein interaction networks, which we call "drug-protein interaction signatures" from the integration of large-scale heterogeneous data of drugs and proteins. We develop a new efficient algorithm for extracting informative drug-protein interaction signatures from the integration of large-scale heterogeneous data of drugs and proteins, which is made possible by space-efficient representations for fingerprints of drug-protein pairs and sparsity-induced classifiers.

De novo design of anticancer peptides by ensemble artificial neural networks.

Membranolytic anticancer peptides (ACPs) are drawing increasing attention as potential future therapeutics against cancer, due to their ability to hinder the development of cellular resistance and their potential to overcome common hurdles of chemotherapy, e.g., side effects and cytotoxicity.

Physicochemical Property Labels as Molecular Descriptors for Improved Analysis of Compound-Protein and Compound-Compound Networks.

Small molecules can be represented in various file formats, (1) one-line systems such as SMILES (Simplified Molecular Input Line Entry System) and InChI (International Chemical Identifier) and (2) table systems such as the molfiles, SDF (Structure Data File), and KCF (KEGG Chemical Function). KCF and KCF-S (KEGG Chemical Function-and-Substructures) apply physicochemical property labels on the representations of small molecules, and contribute to improved analysis of compound-protein networks including drug-target interaction, and compound-compound networks including metabolic pathways. In this chapter, the main concepts, usage, and some example applications of the KCFCO and KCF-S packages are explained.

Data integration aids understanding of butterfly-host plant networks.

Although host-plant selection is a central topic in ecology, its general underpinnings are poorly understood. Here, we performed a case study focusing on the publicly available data on Japanese butterflies. A combined statistical analysis of plant-herbivore relationships and taxonomy revealed that some butterfly subfamilies in different families feed on the same plant families, and the occurrence of this phenomenon more than just by chance, thus indicating the independent acquisition of adaptive phenotypes to the same hosts.

Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics.

The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines.

Metabolic pathway reconstruction strategies for central metabolism and natural product biosynthesis.

Metabolic pathway reconstruction presents a challenge for understanding metabolic pathways in organisms of interest. Different strategies, , reference-based vs. , must be used for pathway reconstruction depending on the availability of well-characterized enzymatic reactions.

Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction.

Motivation: Metabolic pathways are an important class of molecular networks consisting of compounds, enzymes and their interactions. The understanding of global metabolic pathways is extremely important for various applications in ecology and pharmacology. However, large parts of metabolic pathways remain unknown, and most organism-specific pathways contain many missing enzymes.

Identification of Enzyme Genes Using Chemical Structure Alignments of Substrate-Product Pairs.

Although there are several databases that contain data on many metabolites and reactions in biochemical pathways, there is still a big gap in the numbers between experimentally identified enzymes and metabolites. It is supposed that many catalytic enzyme genes are still unknown. Although there are previous studies that estimate the number of candidate enzyme genes, these studies required some additional information aside from the structures of metabolites such as gene expression and order in the genome.

Large-Scale Prediction of Beneficial Drug Combinations Using Drug Efficacy and Target Profiles.

The identification of beneficial drug combinations is a challenging issue in pharmaceutical and clinical research toward combinatorial drug therapy. In the present study, we developed a novel computational method for large-scale prediction of beneficial drug combinations using drug efficacy and target profiles. We designed an informative descriptor for each drug-drug pair based on multiple drug profiles representing drug-targeted proteins and Anatomical Therapeutic Chemical Classification System codes.

Virus proteins similar to human proteins as possible disturbance on human pathways.

Cancer is not rare anywhere in the world now, and the global burden of cancer continues to increase largely every year. Previous research on infections and cancers reported that, about 17.8 % of the cancers worldwide, which are over 1.

Metabolome-scale de novo pathway reconstruction using regioisomer-sensitive graph alignments.

Motivation: Recent advances in mass spectrometry and related metabolomics technologies have enabled the rapid and comprehensive analysis of numerous metabolites. However, biosynthetic and biodegradation pathways are only known for a small portion of metabolites, with most metabolic pathways remaining uncharacterized.

Results: In this study, we developed a novel method for supervised de novo metabolic pathway reconstruction with an improved graph alignment-based approach in the reaction-filling framework.

Benchmarking a Wide Range of Chemical Descriptors for Drug-Target Interaction Prediction Using a Chemogenomic Approach.

The identification of drug-target interactions, or interactions between drug candidate compounds and target candidate proteins, is a crucial process in genomic drug discovery. In silico chemogenomic methods are recently recognized as a promising approach for genome-wide scale prediction of drug-target interactions, but the prediction performance depends heavily on the descriptors and similarity measures of drugs and proteins. In this paper, we investigated the performance of various descriptors and similarity measures of drugs and proteins for the drug-target interaction prediction using a chemogenomic approach.

PIERO ontology for analysis of biochemical transformations: effective implementation of reaction information in the IUBMB enzyme list.

Genomics is faced with the issue of many partially annotated putative enzyme-encoding genes for which activities have not yet been verified, while metabolomics is faced with the issue of many putative enzyme reactions for which full equations have not been verified. Knowledge of enzymes has been collected by IUBMB, and has been made public as the Enzyme List. To date, however, the terminology of the Enzyme List has not been assessed comprehensively by bioinformatics studies.

Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach.

Motivation: Metabolic pathway analysis is crucial not only in metabolic engineering but also in rational drug design. However, the biosynthetic/biodegradation pathways are known only for a small portion of metabolites, and a vast amount of pathways remain uncharacterized. Therefore, an important challenge in metabolomics is the de novo reconstruction of potential reaction networks on a metabolome-scale.

WURCS: the Web3 unique representation of carbohydrate structures.

In recent years, the Semantic Web has become the focus of life science database development as a means to link life science data in an effective and efficient manner. In order for carbohydrate data to be applied to this new technology, there are two requirements for carbohydrate data representations: (1) a linear notation which can be used as a URI (Uniform Resource Identifier) if needed and (2) a unique notation such that any published glycan structure can be represented distinctively. This latter requirement includes the possible representation of nonstandard monosaccharide units as a part of the glycan structure, as well as compositions, repeating units, and ambiguous structures where linkages/linkage positions are unidentified.

Inferring protein domains associated with drug side effects based on drug-target interaction network.

Background: Most phenotypic effects of drugs are involved in the interactions between drugs and their target proteins, however, our knowledge about the molecular mechanism of the drug-target interactions is very limited. One of challenging issues in recent pharmaceutical science is to identify the underlying molecular features which govern drug-target interactions.

Results: In this paper, we make a systematic analysis of the correlation between drug side effects and protein domains, which we call "pharmacogenomic features," based on the drug-target interaction network.

KCF-S: KEGG Chemical Function and Substructure for improved interpretability and prediction in chemical bioinformatics.

Background: In order to develop hypothesis on unknown metabolic pathways, biochemists frequently rely on literature that uses a free-text format to describe functional groups or substructures. In computational chemistry or cheminformatics, molecules are typically represented by chemical descriptors, i.e.