Journal of Chemical Information and Modeling 最新文献

Molecularly imprinted polymers (MIPs) have emerged as bespoke materials with versatile molecular applications. In this study, we propose a proof of concept for a methodology employing molecular dynamics (MD) simulations to guide the selection of functional monomers for curcuminoid binding in MIPs. Curcumin, demethoxycurcumin, and bisdemethoxycurcumin are phenolic compounds widely employed as spices, pigments, additives, and therapeutic agents, representing the three main curcuminoids of interest. Through MD simulations, we investigated prepolymerization mixtures composed of various functional monomers, including acrylamide (ACA), acrylic acid (AA), methacrylic acid (MAA), and N-vinylpyrrolidone (NVP), with ethylene glycol dimethacrylate (EGDMA) as the cross-linker and acetonitrile as the solvent. Curcumin was selected as the template molecule due to its structural similarity to the other curcuminoids. Notably, the prepolymerization mixture containing NVP as the functional monomer demonstrated superior molecular recognition capabilities toward curcumin. This observation was supported by higher functional monomer molecules surrounding the template, a lower total nonbonded energy between the template and monomer, and a greater number of hydrogen bonds in the aggregate. These findings suggest a stronger affinity between the functional monomer NVP and the template. We synthesized, characterized, and conducted binding tests on the MIPs to validate the MD simulation results. The experimental binding tests confirmed that the MIP-NVP exhibited higher binding capacity. Consequently, based on MD simulations, our computational methodology effectively guided the selection of the functional monomer, leading to MIPs with binding capacity for curcuminoids. The outcomes of this study provide a valuable reference for the rational design of MIPs through MD simulations, facilitating the selection of components for MIPs. This computational approach holds the potential for extension to other templates, establishing a robust methodology for the rational design of MIPs.

Small molecule antioxidants can inhibit or retard oxidation reactions and protect against free radical damage to cells, thus playing a key role in food, cosmetics, pharmaceuticals, the environment, as well as materials. Experimentally driven antioxidant discovery is a major paradigm, and computationally assisted antioxidants are rarely reported. In this study, a functional-group-based alternating multitask self-supervised molecular representation learning method is proposed to simultaneously predict the antioxidant activities of small molecules for eight commonly used in vitro antioxidant assays. Extensive evaluation results reveal that compared with the baseline models, the multitask FG-BERT model achieves the best overall predictive performance, with the highest average F1, BA, ROC-AUC, and PRC-AUC values of 0.860, 0.880, 0.954, and 0.937 for the test sets, respectively. The Y-scrambling testing results further demonstrate that such a deep learning model was not constructed by accident and that it has reliable predictive capabilities. Additionally, the excellent interpretability of the multitask FG-BERT model makes it easy to identify key structural fragments/groups that contribute significantly to the antioxidant effect of a given molecule. Finally, an online antioxidant activity prediction platform called AOP (freely available at https://aop.idruglab.cn/) and its local version were developed based on the high-quality multitask FG-BERT model for experts and nonexperts in the field. We anticipate that it will contribute to the discovery of novel small-molecule antioxidants.

Ferroptosis is an iron-dependent programmed cell death characterized by lipid peroxidation that is linked to the pathophysiological processes in many diseases, such as neurodegenerative diseases, cancers, ischemia-reperfusion injuries, and organ damages. Many proteins are associated with ferroptosis signal transduction pathways. Novel chemical compounds are demanded to explore and regulate these pathways. Therefore, a ferroptosis ligand database, which holds relations among chemical structures, targets, bioactivities, and diseases, is needed for discovering and designing new ferroptosis regulators. This work reports FerroLigandDB, a manually curated database for small-molecular ferroptosis regulators. The database comprises 466 ferroptosis inducer entries (with 380 unique molecular structures) and 539 ferroptosis inhibitor entries (with 468 unique molecular structures) (note: one compound can be recorded as multiple entries due to the different assays). Each ferroptosis ligand entry is detailed with compound IDs, structure attributes, bioactivity values, test objects, target information, associated diseases, and references. The fields in the FerroLigandDB database implicitly contain relationships among chemical structures, bioactivities, targets, and diseases. Thus, FerroLigandDB is a comprehensive resource for scientists to design and discover novel ferroptosis regulators. The user interface of FerroLigandDB is implemented with query features and data visualization facilities. With compound identifiers, the compounds are linked to the records of other chemoinformatics databases (such as PubChem and SciFinder). The FerroLigandDB database is freely accessible at http://ferr.gulab.org.cn/.

Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating and comparing predictions from various machine learning (ML) and deep learning (DL) techniques is challenging but crucial for anticancer drug research. We conducted a comprehensive analysis of 15 ML and 10 DL models, including the models released after 2022, and found that support vector machines (SVMs) with feature combination and selection significantly enhance overall performance. DL models, especially convolutional neural networks (CNNs) with light gradient boosting machine (LGBM) based feature selection approaches, demonstrate improved characterization. Assessment using a new test data set (ACP10) identifies ACPred, MLACP 2.0, AI4ACP, mACPred, and AntiCP2.0_AAC as successive optimal predictors, showcasing robust performance. Our review underscores current prediction tool limitations and advocates for an omnidirectional ACP prediction framework to propel ongoing research.

Discovered in the 1920s, cytochrome bd is a terminal oxidase that has received renewed attention as a drug target since its atomic structure was first determined in 2016. Only found in prokaryotes, we study it here as a drug target for Mycobacterium tuberculosis (Mtb). Most previous drug discovery efforts toward cytochrome bd have involved analogues of the canonical substrate quinone, known as Aurachin D. Here, we report six new cytochrome bd inhibitor scaffolds determined from a computational screen and confirmed on target activity through in vitro testing. These scaffolds provide new avenues for lead optimization toward Mtb therapeutics.

Optimization techniques play a pivotal role in advancing drug development, serving as the foundation of numerous generative methods tailored to efficiently design optimized molecules derived from existing lead compounds. However, existing methods often encounter difficulties in generating diverse, novel, and high-property molecules that simultaneously optimize multiple drug properties. To overcome this bottleneck, we propose a multiobjective molecule optimization framework (MOMO). MOMO employs a specially designed Pareto-based multiproperty evaluation strategy at the molecular sequence level to guide the evolutionary search in an implicit chemical space. A comparative analysis of MOMO with five state-of-the-art methods across two benchmark multiproperty molecule optimization tasks reveals that MOMO markedly outperforms them in terms of diversity, novelty, and optimized properties. The practical applicability of MOMO in drug discovery has also been validated on four challenging tasks in the real-world discovery problem. These results suggest that MOMO can provide a useful tool to facilitate molecule optimization problems with multiple properties.

Venezuelan equine encephalitis virus (VEEV) is a highly virulent pathogen whose nuclear localization signal (NLS) sequence from capsid protein binds to the host importin-α transport protein and blocks nuclear import. We studied the molecular mechanisms by which two small ligands, termed I1 and I2, interfere with the binding of VEEV's NLS peptide to importin-α protein. To this end, we performed all-atom replica exchange molecular dynamics simulations probing the competitive binding of the VEEV coreNLS peptide and I1 or I2 ligand to the importin-α major NLS binding site. As a reference, we used our previous simulations, which examined noncompetitive binding of the coreNLS peptide or the inhibitors to importin-α. We found that both inhibitors completely abrogate the native binding of the coreNLS peptide, forcing it to adopt a manifold of nonnative loosely bound poses within the importin-α major NLS binding site. Both inhibitors primarily destabilize the native coreNLS binding by masking its amino acids rather than competing with it for binding to importin-α. Because I2, in contrast to I1, binds off-site localizing on the edge of the major NLS binding site, it inhibits fewer coreNLS native binding interactions than I1. Structural analysis is supported by computations of the free energies of the coreNLS peptide binding to importin-α with or without competition from the inhibitors. Specifically, both inhibitors reduce the free energy gain from coreNLS binding, with I1 causing significantly larger loss than I2. To test our simulations, we performed AlphaScreen experiments measuring IC50 values for both inhibitors. Consistent with in silico results, the IC50 value for I1 was found to be lower than that for I2. We hypothesize that the inhibitory action of I1 and I2 ligands might be specific to the NLS from VEEV's capsid protein.