MolHFCNet: Enhancing Molecular Graph Representations with Hierarchical Feature Combining and Hybrid Pretraining

Accurate molecular property prediction is a critical challenge with wide-ranging applications in chemistry, materials science, and drug discovery. Molecular representation methods, including fingerprints and graph neural networks (GNNs), achieve state-of-the-art results by effectively deriving features from molecular structures. However, these methods often overlook decades of accumulated semantic and contextual knowledge. Recent advancements in large language models (LLMs) demonstrate remarkable reasoning abilities and prior knowledge across scientific domains, leading us to hypothesize that LLMs can generate rich molecular representations when guided to reason in multiple perspectives. To address these gaps, we propose M^2LLM, a multi-view framework that integrates three perspectives: the molecular structure view, the molecular task view, and the molecular rules view. These views are fused dynamically to adapt to task requirements, and experiments demonstrate that M^2LLM achieves state-of-the-art performance on multiple benchmarks across classification and regression tasks. Moreover, we demonstrate that representation derived from LLM achieves exceptional performance by leveraging two core functionalities: the generation of molecular embeddings through their encoding capabilities and the curation of molecular features through advanced reasoning processes.

Artificial intelligence (AI) has grown widely into diverse areas of research over the last decade. Publications distributed over the years disclose that biochemistry and analytical chemistry are incorporating AI to the utmost level. Enormous complex data sets are produced by analytical instruments comprising a treasure of information which is very helpful to analytical chemists in characterization. Despite this, we lack capabilities in data analysis and algorithms, which restricts our aptitude to discover and employ this data entirely. Machine learning and AI are being instigated to deal with this current challenge by accelerating several other applications in analytical chemistry. A critical valuation of illustrative reports amalgamating AI with analytical chemistry, spectroscopies, and several sensors has been discussed widely. This comprehensive review evaluates the AI advancement in data interpretation, the assistance of AI in analyte identification and quantification in proteomics and metabolomics, AI databases for primary and secondary metabolites information, regulatory and ethical guidelines in analytical chemistry incorporating AI, machine learning applications in drug discovery, agriculture, cosmetic, and food and separation techniques. In summary, this current review puts forward a thorough examination concerning the advancements of AI in different domains of chemistry and strives to facilitate comprehension of its forthcoming trajectories.

SSHF-DTI: Leveraging structural similarity and hierarchical features through a fusion network for drug-target interaction prediction.

Graph-based molecular representation learning is essential for predicting molecular properties in drug discovery and materials science. Despite its importance, current approaches struggle with capturing the intricate molecular relationships and often rely on limited chemical knowledge during training. Multimodal fusion, which integrates information from graph and other data sources together, has emerged as a promising approach for enhancing molecular property prediction. However, existing studies explore only a narrow range of modalities, and the optimal integration stages for multimodal fusion remain largely unexplored. Furthermore, the reliance on auxiliary modalities poses challenges, as such data is often unavailable in downstream tasks. Here, we present MMFRL (Multimodal Fusion with Relational Learning), a framework designed to address these limitations by leveraging relational learning to enrich embedding initialization during multimodal pre-training. MMFRL enables downstream models to benefit from auxiliary modalities, even when these are absent during inference. We also systematically investigate modality fusion at early, intermediate, and late stages, elucidating their unique advantages and trade-offs. Using the MoleculeNet benchmarks, we demonstrate that MMFRL significantly outperforms existing methods with superior accuracy and robustness. Beyond predictive performance, MMFRL enhances explainability, offering valuable insights into chemical properties and highlighting its potential to transform real-world applications in drug discovery and materials science.

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

Exploring new horizons: Empowering computer-assisted drug design with few-shot learning

In recent years, machine learning models have shown substantial progress in predicting molecular properties. However, integrating molecular graph structures with sequence information continues to present a significant challenge. In this paper, we introduce Multi-MoleScale, a novel multi-scale framework designed to address this challenge. By combining Graph Contrastive Learning (GCL) with sequence-based models like BERT, Multi-MoleScale enhances the prediction of molecular properties by capturing both structural and contextual representations of molecules. Specifically, the model leverages GCL to effectively capture the intrinsic graph-based features of molecules while utilizing BERT’s pretraining capabilities to learn the contextual relationships within molecular sequences. The contrastive learning component enables Multi-MoleScale to distinguish between relevant and irrelevant molecular features, thereby enhancing its predictive accuracy across diverse molecular types. To assess the performance of our method, we conducted experiments on several widely used public datasets, including 12 molecular property datasets, the ADMET dataset, and 14 breast cancer cell line datasets. The results show that Multi-MoleScale consistently outperforms existing deep learning and self-supervised learning approaches. Notably, the model does not require handcrafted features, making it highly adaptable and versatile for a variety of molecular discovery tasks. This makes Multi-MoleScale a promising tool for applications in drug discovery, materials science, and other molecular research fields. Our data and code are available at https://github.com/pdssunny/Multi-MoleScale. Supplementary InformationThe online version contains supplementary material available at 10.1186/s13321-025-01126-w.

This research introduces two novel topological indices, the entire Albertson index and the entire sigma index, as quantitative measures of molecular irregularity. The indices are defined by precise mathematical formulas and their behavior is analyzed across a diverse range of graph families. To evaluate the predictive capabilities of the proposed indices, we compare their performance with established irregularity indices in the modeling of molecular properties. Correlations with physicochemical properties, including the boiling point, melting point, and molecular volume, are investigated. Specific expressions for these indices are derived for various molecular structures, such as bridge molecules, polyomino chains of n-cycles, triangular benzenoid graphs, graphene, and dendrimer stars D3[n]. The findings of this study contribute significantly to the field of chemical graph theory by providing novel tools to understand and predict molecular behavior. The entire irregularity indices have potential applications in drug discovery, materials science, and other areas where molecular properties are crucial.

Undoubtedly, deep learning (DL) has made striking progress in different artificial intelligence research fields in the bygone decade. DL is a subset of artificial intelligence, where models depict mathematical changes over a wide range of layers. DL is rapidly becoming the state-of-the-art execution in a variety of assignments and it has indicated huge potential in different fields, for example, speech recognition, computer vision and natural language processing. It has additionally been effectively applicable in drug discovery which is one of the areas that can acquire many advantages from the accomplishment of DL. Drug discovery and advancement are among the main translational science activities that add to human well-being and prosperity. This chapter highlights fundamentals of DL, challenges, opportunities, and applications in drug discovery.

Review: [recent developments towards the construction of compound libraries and their application in drug and probe discovery; 86 refs.

Synthetic methods for unsymmetrical disulfides are greatly needed owing to their applications in drug discovery, linker chemistry, and materials sciences. In this study, a new shelf-stable and easy-to-prepare bilateral disulfurating platform molecule, N-(morpholine-4-dithio)phthalimide, has been developed for the divergent synthesis of unsymmetrical disulfides. The amino and imide leaving groups of this reagent can be orthogonally transformed. Under acidic conditions, the amino moiety undergoes selective protonation and thus can be displaced by various carbon nucleophiles, such as allyl trimethylsilanes, alkynyl silanes, and electron-rich arenes, leaving the phthalimide moiety untouched. Meanwhile, the phthalimide leaving group is amenable to substitution under basic or neutral conditions. The combination of these transformations provides rapid access to diverse unsymmetrical disulfides through two C-S bond-forming reactions.

Nitrate esters hold pivotal roles in pharmaceuticals, energetic materials, and atmospheric processes, motivating the development of efficient synthesis routes. Here, we present a novel catalytic method for the synthesis of nitrates via the direct O‐nitration of alcohols, addressing limitations of current traditional methods. Leveraging bench‐stable and recoverable N,6‐dinitrosaccharin reagent, our catalytic strategy employs magnesium triflate to achieve mild and selective O‐nitration of alcohols, offering broad substrate scope and unprecedentedly large functional group tolerance (e.g. alkenes, alkynes, carbonyls). DFT mechanistic studies reveal a dual role of the magnesium catalyst in the activation of both the nitrating reagent and the alcohol substrate. They also unveil a barrierless proton transfer upon formation of a widely‐accepted – yet elusive in solution – nitrooxonium ion intermediate. Overall, our work contributes to the development of mild, selective, and sustainable approaches to nitrates synthesis, with potential applications in drug discovery, materials science, and environmental chemistry.

This review explores recent advancements in synthesizing quinoid heteroaryls, namely quinazoline and quinoline, vital in chemistry due to their prevalence in natural products and pharmaceuticals. It emphasizes the rapid, highly efficient, and economically viable synthesis achieved through gold-catalyzed cascade protocols. By investigating methodologies and reaction pathways, the review underscores exceptional yields attainable in the synthesis of quinoid heteroaryls. It offers valuable insights into accessing these complex structures through efficient synthetic routes. Various strategies, including cyclization, heteroarylation, cycloisomerization, cyclo-condensation, intermolecular and intramolecular cascade reactions, are covered, highlighting the versatility of gold-catalyzed approaches. The comprehensive compilation of different synthetic approaches and elucidation of reaction mechanisms contribute to a deeper understanding of the field. This review paves the way for future advancements in synthesizing quinoid heteroaryls and their applications in drug discovery and materials science.

Multicomponent reactions, which involve the reaction of three or more components in a single step to efficiently produce a new molecular structure, have received growing attention in a range of research areas. In this study, four- and five-component reactions incorporating alkenes, carboxylic acids, alkyl radicals, and nitriles are developed for the imidoalkylation of alkenes with perfect regioselectivity. More specifically, this approach involves a photoredox reaction based on a catalytic indirect reductive quenching system. Various drug molecules and amino acids were demonstrated to be compatible substrates for this reaction, thereby enabling the synthesis of multifunctionalized derivatives. Notably, five individual components were assembled with perfect regioselectivity via the reaction of a radical species in the presence of a polarity-matched alkene. Mechanistic studies and density functional theory calculations suggested that this multicomponent reaction involves a cascade of Ritter-type substitutions and Mumm rearrangements. This method exhibits immense potential for use in the creation of structurally diverse and biologically relevant molecules with defined connectivities, thereby holding promise for applications in drug discovery and materials science.

Activity- and reactivity-based proteomics: Recent technological advances and applications in drug discovery