Channelrhodopsins (ChRs) are photoreceptors that function as light-gated ion channels. Over the last two decades, they have become essential tools in optogenetics, enabling precise manipulation of neurons, neural circuits, and animal behavior through light. Although structural studies have provided important mechanistic insights into channelrhodopsins, a detailed understanding of their ion conduction

Exploring the interaction configurations between substrates and atomic or molecular systems is crucial for various scientific and technological applications, such as characterizing catalytic reactions, solvation structures, and molecular interactions. Traditional approaches for generating substrate-reactant configurations often rely on chemical intuition, symmetry operations, or random initial states

We introduce OxPot, a comprehensive open-access data set comprising over 15 thousand chemically diverse organic molecules. Leveraging the precision of DFT-derived highest occupied molecular orbital energies (EHOMO), OxPot serves as a robust platform for accelerating the prediction of oxidation potential (Eox). Using the PBE0 hybrid functional and cc-pVDZ basis set, we establish a strong near-linear

The performance and reliability of machine learning (ML)-quantitative structure-property relationship (QSPR) models depend on the quality, size, and diversity of the data set used for model training. In this study, we manually curated a large-scale data set containing 3120 donor-acceptor (D-A) conjugated polymers (CPs) by selecting the most utilized 60 donors and 52 acceptors. This data set serves

Deep learning models have demonstrated their potential in learning effective molecular representations critical for drug property prediction and drug discovery. Despite significant advancements in leveraging multimodal drug molecule semantics, existing approaches often struggle with challenges such as low-quality data and structural complexity. Large language models (LLMs) excel in generating high-quality

Effective drug safety assessment, guided by the 3R principle (Replacement, Reduction, Refinement) to minimize animal testing, is critical in early drug development. Drug-induced liver injury (DILI), particularly drug-induced cholestasis (DIC), remains a major challenge. This study introduces a computational method for predicting DIC by integrating PubChem substructure fingerprints with biological data

Nucleoside analogues (NAs) constitute a class of compounds that mirror the structure of natural nucleosides but undergo chemical modifications, rendering them an important compound resource for drug development as exogenous substitutes for natural nucleosides. Nevertheless, the design of novel NA-based drugs remains a great challenge due to the complexity of asymmetric synthesis and restrained chemical

The optimal enzyme pH is a critical factor that directly influences the catalytic efficiency of the enzymes. Accurate computational prediction of the optimal pH can greatly advance our understanding and design of enzymes for diverse scientific and industrial applications. However, current prediction tools often fall short in terms of accuracy and robustness. In this study, we propose OpHReda, a novel

Suppression of Tumorigenicity 2 (ST2) is a member of the IL-1 receptor family, which includes transmembrane (ST2L) and soluble (sST2) isoforms. sST2 functions as a decoy receptor for Interleukin-33 (IL-33), thereby blocking the activation of the IL-33/ST2L signaling axis, which is essential for tissue repair and immune regulation. Clinical evidence indicates that elevated sST2 levels are associated

To fulfill functions for differentially regulating the downstream signaling pathways, functional ligands (i.e., agonists or antagonists) targeting nuclear receptors (NRs) are designed to stabilize different conformations (active or inactive) of the proteins. However, in practical applications, it is usually difficult to determine the molecular category of an NR ligand because these molecules all bind

EphA2 receptor tyrosine kinase is overexpressed in many solid tumors and serves as a key driver of tumorigenesis and metastasis. It is highly expressed in glioblastoma multiforme, the most aggressive brain tumor in adults, and in its stem cells [glioblastoma stem cells (GSCs)], which contribute to treatment resistance and tumor relapse. In a previous study, we used the Systematic Evolution of Ligands

Docking-based virtual screening tools customized to mine ultralarge chemical spaces are consistently reported to yield both higher hit rates and more potent ligands than that achieved by conventional docking of smaller million-sized compound libraries. This remarkable achievement is however counterbalanced by the absolute necessity to design an efficient postprocessing of the millions of potential

The study of synergistic drug combinations is vital in cancer treatment, enhancing efficacy, reducing resistance, and minimizing side effects through complementary drug actions. Drug-drug interaction (DDI) analysis offers essential theoretical support, and with the rise of data science, intelligent algorithms are increasingly replacing traditional in vitro screening for predicting potential DDIs. Considering

3D generative models have shown great potential in structure-based drug design for generating ligands tailored to specific protein binding pockets. However, existing methods primarily emphasize ligand-target geometric interactions and binding affinity prediction, often overlooking intrinsic physicochemical principles driving protein-ligand interactions as well as critical pharmaceutical properties

Free energy simulations play a pivotal role in diverse biological applications, including enzyme design, drug discovery, and biomolecular engineering. The characterization of high-dimensional free energy surfaces underlying complex enzymatic mechanisms necessitates extensive sampling through umbrella sampling or string method simulations. Accurate ranking of target-binding free energies across large

Drug-likeness is essential in drug discovery, indicating the potential of a compound to become a successful therapeutic. However, existing rule-based and machine learning methods are limited by their reliance on hand-crafted features, poor generalizability across chemical spaces, and insufficient adaptability to the diverse contexts of drug development. To overcome these limitations, we introduce an

Mycotoxins are potent triggers of hepatocellular carcinoma (HCC) due to their intricate interplay with cellular macromolecules and signaling pathways. This study integrates machine learning and biomolecular analyses to elucidate the mechanisms underlying mycotoxin-induced hepatocarcinogenesis. Using a data set of 1767 mycotoxins and 1706 non-mycotoxin fungal metabolites, we evaluated 51 machine learning

Dysregulated calpain-1 and calpain-2 protease activity linked to several diseases has encouraged efforts to explore inhibiting calpain to provide therapeutic benefits. However, there are currently no clinically approved drugs that specifically target calpain functionality. To address this unmet need, we carried out in silico drug discovery efforts to identify small molecules capable of modulating calpain

Significant advancements have been made in utilizing artificial intelligence to learn to generate molecular conformations, which has greatly facilitated the discovery of drug molecules. In particular, the rapid development of diffusion models has led to major progress in the field of molecular generation. However, existing molecular diffusion generative models often treat atoms within a molecule as

Molecular property prediction (MPP) plays a critical role in drug design and discovery. Due to the multimodal nature of molecular data (e.g., 1D SMILES strings and 2D molecular graphs), multimodal information fusion can generally achieve better performance than using single-modality molecular data. On the other hand, with the rise of large language models (LLMs), increasing efforts have been made to

Over the years, molecular dynamics (MD) simulations have been employed in the study of carbohydrates, with force fields such as CHARMM, AMBER/GLYCAM, and GROMOS. Although these force fields have achieved considerable success and played a pivotal role in our understanding of carbohydrate chemistry, growing interest has emerged in incorporating polarization effects to enhance the accuracy of simulations

We propose an atomistic mechanism suggesting that fundamental plant processes, including seed germination, root elongation, and flower and fruit production, may be regulated by phytohormones such as Gibberellin A1 (GA1) binding to the GCR1 plant G-protein-coupled receptor. This parallels the central roles of GPCRs in animals for vision, taste, smell, pain, depression, and nerve signaling, among others

EnzyDock is a multistate, multiscale CHARMM-based docking program which enables mechanistic docking, i.e., modeling enzyme reactions by docking multiple reaction states, like substrates, intermediates, transition states, and products to the enzyme, in addition to standard protein-ligand docking. To achieve docking of multiple reaction states with similar poses (i.e., consensus docking), EnzyDock employs

Understanding the binding interactions within protein-peptide complexes is crucial for elucidating key physicochemical phenomena in biological systems. Among the outcomes of these interactions, biomolecular condensates have recently emerged as vital players in various cellular functions including signaling. Complexes such as PRM:SH3 are known to undergo condensation, yet the chemical interactions and

In early-stage drug design, machine learning models often rely on compressed representations of data, where raw experimental results are distilled into a single metric per molecule through curve fitting. This process discards valuable information about the quality of the curve fit. In this study, we incorporated a fit-quality metric into machine learning models to capture the reliability of metrics

Extensive research has shown that microRNAs (miRNAs) play a crucial role in cancer progression, treatment, and drug resistance. They have been recognized as promising potential therapeutic targets for overcoming drug resistance in cancer treatment. However, limited attention has been paid to predicting the association between miRNAs and drugs by computational methods. Existing approaches typically

Although there are over 100,000 distinct human metabolites, their biological significance is often not fully appreciated. Metabolites can reshape the protein pockets to which they bind by COLIG formation, thereby influencing enzyme kinetics and altering the monomer-multimer equilibrium in protein complexes. Binding a common metabolite to a set of protein monomers or multimers results in metabolic entanglements

Mycobacterium abscessus (MAB) infections pose a significant treatment challenge due to their intrinsic resistance to antibiotics, requiring prolonged multidrug regimens with limited success and frequent relapses. tRNA (m1G37) methyltransferase (TrmD), an enzyme essential for maintaining the reading frame during protein synthesis in MAB and other mycobacteria, is a potential therapeutic target for identifying

The quantitative characterization of residue contributions to protein-protein binding across extensive flexible interfaces poses a significant challenge for biophysical computations. It is attributable to the inherent imperfections in the experimental structures themselves, as well as to the lack of reliable computational tools for the evaluation of all types of noncovalent interactions. This study

Drug repositioning, which identifies novel therapeutic applications for existing drugs, offers a cost-effective alternative to traditional drug development. However, effectively capturing the complex relationships between drugs and diseases remains challenging. We present HGCL-DR, a novel heterogeneous graph contrastive learning framework for drug repositioning that effectively integrates global and

Fuel design is usually "forward": candidate molecular structures are designed first, and then their properties are predicted for screening. Owing to the large latent space of organic molecules (1060 order), reverse design by giving target fuel properties is urgently needed. However, it is hardly realized due to the unknown complex rule of the structure-property relationship. In this work, reverse design

DNA's structural flexibility plays a crucial role in various biological functions such as gene replication, repair, and regulation as well as DNA-protein recognition. We investigate the bending free energy of short DNA helices, including d(5'-(CG)7C-3')2 in A-, B-, and Z-forms, and C- and G-rich trinucleotide repeat helices, using orientation quaternions with enhanced sampling methods. The orientation

As a mainstream technology in modern drug discovery, molecular docking methodologies enable precise and efficient identification of lead compounds within large chemical repositories to improve drug development efficiency and reduce costs. The exponential growth of chemical databases has substantially expanded drug discovery resources while improving the identification rates of true positives in lead

Over the past decade, improvements in computing power and theoretical approaches have enabled high-throughput computational investigations of systems. In this work, we present the development of a simple automated computational protocol for the study of molecular substitutions to known molecules, which minimizes human error and effort while capitalizing on existing calculations to optimize computational

One recurring question when choosing which molecules to select for investigation is that of molecular complexity: is there a price to pay for complexity in terms of synthesis difficulty, and does complexity have anything to do with biological properties? In the chemical space of small organic molecules enumerated from mathematical graphs in the GDBs (Generated DataBases), most compounds are too complex

Metabolites are small molecules produced during organism metabolism, with their abnormal concentrations closely linked to the onset and progression of various diseases. Accurate prediction of metabolite-disease associations is crucial for early diagnosis, mechanistic exploration, and treatment optimization. However, existing algorithms often overlook the integration of node features and neglect the

Metalloproteins play crucial physiological roles across all domains of life, relying on metal ions for structural stability and catalytic activity. In recent years, computational approaches have emerged as powerful and increasingly reliable tools for predicting metal-binding sites in metalloproteins, enabling their application in the challenging field of metalloproteomics. Given the growing number

Discovering microbes underlying disease traits opens up opportunities for the diagnosis and effective treatment of diseases. However, traditional methods are often based on biological experiments, which are not only time-consuming but also costly, driving the need for computational frameworks that can accelerate the discovery of these associations. Motivated by these challenges, we propose an innovative

Glutaminyl cyclases are responsible for N-terminal pyroglutamate modifications of various protein/peptide substrates, influencing their metabolic stability or biological functions. However, the precise chemical pathways by which glutaminyl cyclases catalyze the conversion of N-terminal glutamine/glutamate to pyroglutamate are not yet fully understood. We initially identified the catalytically essential

Molecular docking is a widely used method within the in silico compound screening process of modern drug discovery. The accuracy of this method for predicting high-affinity small-molecule binders for a target protein from a large chemical library can be substantially improved by combining individual docking tools for cross-validation. This traditional consensus strategy typically relies on averaging

Interactions between intrinsically disordered proteins (IDPs) are crucial for biological processes, such as intracellular liquid-liquid phase separation (LLPS). Experiments (e.g., NMR) and simulations used to study IDP interactions encounter a variety of difficulties, highlighting the necessity to develop relevant machine learning methods. However, reliable machine learning methods face the challenge

Extracting material property data from scientific text is pivotal for advancing data-driven research in chemistry and materials science; however, the extensive annotation effort required to produce training data for named entity recognition (NER) models for this task often makes it a barrier to extracting specialized data sets. In this work, we present a comparative study of the conventional, supervised

The identification and analysis of pockets are crucial for understanding the functional mechanisms and therapeutic potential of proteins. However, it is challenging to track the dynamic characteristics of the pockets. In this paper, we present a method for the visualization and analysis of protein pocket dynamics called PocketSCP. Initially, the representation of lining amino acid atoms is proposed

The antigen-antibody interaction represents a critical mechanism in host defense, contributing to pathogen neutralization, tumor surveillance, immunotherapy, and in vitro disease detection. Owing to their exceptional specificity, affinity, and selectivity, antibodies have been extensively utilized in the development of clinical diagnostic, therapeutic, and prophylactic strategies. In this study, we

Current trends in molecular modeling are geared toward increasingly realistic representations of the biological environments reflected in larger, more complex systems. The complexity of the system-building procedure is ideally handled by software that converts user-provided descriptors into system coordinates. This, however, is not a trivial task, as building algorithms use simplifications that result

Trypanosoma cruzi carbonic anhydrase (TcCA) has emerged as a promising therapeutic target for the treatment of Chagas disease. In this study, a sequential virtual screening strategy was employed to identify potential TcCA inhibitors. The workflow consisted of ligand-based virtual screening applied to diverse chemical libraries, followed by target-based molecular docking to refine the selection of compounds

This study provides the first quantitative dissection of the factors influencing 1H NMR chemical shifts δH in strong hydrogen-bonded systems, focusing on solvation, nuclear dynamics, and nuclear delocalization. A novel computational framework was developed, combining static quantum chemical calculations (nonrelativistic and relativistic), ab initio molecular dynamics (AIMD), and three-dimensional numerical

The human G protein-coupled receptor (GPCR) dopamine 2 receptor (D2R) is an essential target of antipsychotic drugs. The modulation of downstream GPCR signaling induced by different agonists, termed functional selectivity, has potentially a great impact on drug discovery and control of side effects. The molecular origin of this modulation is, however, not fully understood. Here, the structural determinants

Machine learning algorithms have played a fundamental role in the development of therapeutic antibodies by being trained on data sets of sequences and/or structures. However, structural data sets remain limited, especially those that include antibody-antigen complexes. Additionally, many of the available structures are not standardized, and antibody-specific databases often do not provide molecular

In molecular property prediction tasks, most methods rely on single-view representations, such as simplified molecular input line entry system (SMILES) strings. Some scholars have attempted to combine two graphical views for joint representation purposes, such as SMILES and molecular graphs, but few have utilized three or more graphical views for molecular representation. Additionally, these methods

Blood-brain barrier (BBB) permeability plays a crucial role in determining drug efficacy in the brain, with the brain-to-plasma unbound partition coefficient (Kp,uu) recognized as a key parameter of BBB permeability in drug development. However, Kp,uu data are scarce and mostly in-house. In predicting Kp,uu the generality and applicability of existing empirical scoring models remain underexplored.

Prediction of drug-induced nephrotoxicity is an important task in the drug discovery and development pipeline. Chemical information-based machine learning models are used in general for nephrotoxicity prediction as a part of computational modeling. Currently, gene expression data are being considered increasingly for prediction of different toxicities, as they can provide mechanistic understanding

Polycyclic aromatic hydrocarbons (PAHs) play a crucial role in astrochemistry, environmental studies, and combustion chemistry, yet interpreting their infrared (IR) spectra remains challenging due to the similarity of spectral features of many molecules. The presumable presence of both neutral and charged PAHs in mixtures complicates spectra interpretation, too. While first-principle calculations provide

In modern nanobeam transmission electron microscopy methods, such as 4D-STEM, a converged electron nanobeam is scanned across a sample. Its 2D scattering pattern is recorded at each sample position, mapping the local sample structure. One of the bottlenecks in electron scattering is the analysis of the scattering data obtained from complex atomic or molecular structures. On the basis of D+ software

Coumarin derivatives have been widely developed and utilized as chromophores and fluorophores in various research fields. In this study, we constructed an experimental database of the optical properties─specifically, absorption and emission wavelengths measured in solutions─and developed a machine learning (ML) model based on Gaussian-weighted graph convolution (GWGC) and subgraph modular input (SMI)

In the process of mining and de novo designing of new enzymes, the solubility of proteins is one of the key factors determining the efficiency of their functional expression. The development of solubility prediction algorithms is important for reducing experimental costs and enhancing the success of protein engineering. However, only a small number of studies have involved the input of protein structural

Silybin A (Slym), the principal bioactive constituent of silymarin, exhibits significant therapeutic potential but suffers from poor bioavailability due to its low aqueous solubility. This study addresses this by employing cyclodextrins (CDs) as cost-effective solubilizers to enhance Slym's solubility through the formation of stable supramolecular complexes. Our findings indicate that while β-CD and

The μ-opioid receptor (μOR) is a class A G Protein-Coupled Receptor (GPCR) targeted by natural and synthetic ligands to provide analgesia to patients with pain of various etiologies. Available opioid medications present several unwanted side effects, stressing the need for safer pain therapies. Despite the attractive proposal that biasing μOR signaling toward G protein pathways would lead to fewer

We explore a suite of generative modeling techniques to efficiently navigate and explore the complex landscapes of odor and the broader chemical space. Unlike traditional approaches, we not only generate molecules but also predict the odor likeliness with a ROC AUC score of 0.97 and assign probable odor labels. We correlate odor likeliness with physicochemical features of molecules using machine learning

Computational prediction of potential drug side effects plays a crucial role in reducing health risks for clinical patients and accelerating drug development. Recent methods have constructed heterogeneous graphs that represent drugs and their side effects, utilizing graph learning strategies such as graph convolutional networks to predict associations between them. However, existing approaches fail