Journal of Cheminformatics最新文献

Nipah Virus (NiV) came into limelight due to an outbreak in Kerala, India. NiV infection can cause severe respiratory and neurological problems with fatality rate of 40–70%. It is a public health concern and has the potential to become a global pandemic. Lack of treatment has forced the containment methods to be restricted to isolation and surveillance. WHO’s ‘R&D Blueprint list of priority diseases’ (2018) indicates that there is an urgent need for accelerated research & development for addressing NiV. In the quest for druglike NiV inhibitors (NVIs) a thorough literature search followed by systematic data curation was conducted. Rigorous data analysis was done with curated NVIs for prioritising curated compounds. Our efforts led to the creation of Nipah Virus Inhibitor Knowledgebase (NVIK), a well-curated structured knowledgebase of 220 NVIs with 142 unique small molecule inhibitors. The reported IC50/EC50 values for some of these inhibitors are in the nanomolar range—as low as 0.47 nM. Of 142 unique small-molecule inhibitors, 124 (87.32%) compounds cleared the PAINS filter. The clustering analysis identified more than 90% of the NVIs as singletons signifying their diverse structural features. This diverse chemical space can be utilized in numerous ways to develop druglike anti-nipah molecules. Further, we prioritised top 10 NVIs, based on robustness of assays, physicochemical properties and their toxicity profiles. All the NVIs related information including their structures, physicochemical properties, similarity analysis with FDA approved drugs and other chemical libraries along with predicted ADMET profiles are freely accessible at https://datascience.imtech.res.in/anshu/nipah/. The NVIK has the provision to submit new inhibitors as and when reported by the community for further enhancement of the NVIs landscape.

Scientific contribution

The NVIK is a dedicated resource for NiV drug discovery containing manually curated NVIs. The NVIs are structurally mapped with known chemical space to identify their structural diversity and recommend strategies for chemical library expansion. Also, in NVIK a combined evidence-based strategy is used to prioritise these inhibitors.

Graphical Abstract

Chemical language models (CLMs) have shown strong performance in molecular property prediction and generation tasks. However, the impact of design choices, such as molecular representation format, tokenization strategy, and model architecture, on both performance and chemical interpretability remains underexplored. In this study, we systematically evaluate how these factors influence CLM performance and chemical understanding. We evaluated models through fine-tuning on downstream tasks and probing the structure of their latent spaces using probing predictors, vector operations, and dimensionality reduction techniques. Although downstream task performance was similar across model configurations, substantial differences were observed in the structure and interpretability of internal representations, highlighting that design choices meaningfully shape how chemical information is encoded. In practice, atomwise tokenization generally improved interpretability, and a RoBERTa-based model with SMILES input remains a reliable starting point for standard prediction tasks, as no alternative consistently outperformed it. These results provide guidance for the development of more chemically grounded and interpretable CLMs.

Graphical Abstract

Natural products continue to play a pioneering role in drug discovery due to their extraordinary chemical and biological diversity. However, their full therapeutic potential remains largely underutilized, hindered by the fragmented documentation of biological origins in existing data resources. Here, we present natural product and biological source atlas (NPBS Atlas), a data resource covers over 218,000 natural products fully annotated with comprehensive biological sources, bioactivities, and references. The database established through systematic text mining and expert manual curation, places special emphasis on curating source organism data through the information of scientific nomenclature, taxonomic classification, source parts, and the source of Traditional Chinese Medicines. NPBS Atlas represents significant advancement in natural product data resources through its unique content, specialized annotations, and featured data, thereby enabling unprecedented exploration of nature-derived chemical diversity through biological context. The web interface of NPBS Atlas is freely available at https://biochemai.cstspace.cn/npbs/.

Graphical Abstract

Surfactant mixtures play a critical role in industries such as drug delivery, cosmetics, firefighting foams, and lubrication, serving as foundational components of the global economy. Their performance hinges on micelle formation, a self-assembly process governed by the critical micelle concentration (CMC), which enables key functions like solubilization, emulsification, and targeted molecular delivery. However, rapidly and accurately predicting the CMC of mixtures remains a significant challenge due to the chemical diversity and nonlinear interactions between surfactants. Here, we introduce an artificial neural network (ANN)-based machine learning framework to predict the CMC of binary surfactant mixtures. Our workflow leverages cheminformatics-derived molecular descriptors for each surfactant component, which are then aggregated using strategies such as concatenation, arithmetic mean, and harmonic mean. We find that pairing the arithmetic mean strategy with ANN yields the best performance, effectively capturing complex molecular interactions and enabling dual predictive capabilities: (1) precise interpolation of CMC values at untested mole fractions within known mixtures, and (2) accurate prediction of complete CMC–composition profiles for entirely novel surfactant combinations. SHAP-based interpretability analysis highlights that features such as hydrophobic surface area, electronic topological descriptors, and headgroup basicity drive model predictions, aligning with core principles of surfactant chemistry and reinforcing the mechanistic validity of our model. Overall, this framework accelerates data-driven surfactant design by reducing experimental burden and enabling rapid, rational optimization of formulations across pharmaceuticals, personal care, environmental remediation, and enhanced oil recovery.

Scientific contribution

This study presents a novel machine learning framework that, for the first time, predicts full critical micelle concentration (CMC)–composition profiles for binary surfactant mixtures, including untrained systems. By strategically combining the features of individual components of mixtures using arithmetic mean, our artificial neural network model deciphers nonlinear interactions between chemically distinct surfactants, enabling accurate and generalizable CMC predictions. Beyond performance gains, this framework facilitates rapid and systematic exploration of formulation space via inverse design and high-throughput screening, establishing a powerful foundation for the rational development of next-generation surfactants with applications in energy, environmental remediation, pharmaceuticals, and biomedical science.

Graphical Abstract

The evaluation of potential drug toxicity is a crucial step in early drug development. in vivo toxicity assessment represents a key challenge that must be addressed before advancing to clinical trials. However, traditional in vivo experiments primarily rely on animal models, raising concerns regarding cost, time efficiency, and ethical considerations. To address these challenges, various computational approaches have been developed to support in vivo toxicity evaluations, though these methods often demonstrate limited generalizability due to data scarcity. In this study, we propose MT-Tox, a knowledge transfer-based multi-task learning model specifically designed for in vivo toxicity prediction that overcomes data scarcity. Our model implements a sequential knowledge transfer strategy across three stages: general chemical knowledge pretraining, in vitro toxicological auxiliary training, and in vivo toxicity fine-tuning. This hierarchical approach significantly improves model performance by systematically leveraging information from both chemical structure and toxicity data sources. MT-Tox outperforms baseline models across three in vivo toxicity endpoints: carcinogenicity, drug-induced liver injury (DILI), and genotoxicity. Through ablation studies and attention analyses, we demonstrate that each knowledge transfer technique makes meaningful contributions to the prediction process. Finally, we demonstrate the real-world application of our model as a prediction tool for early-stage drug discovery through comprehensive DrugBank database screening.

Scientific contribution: We propose a knowledge transfer framework that integrates chemical and in vitro toxicological information to enhance in vivo toxicity prediction in low-data regimes. Our model provides dual-level interpretability across chemical and biological domains through attention mechanism. Moreover, we demonstrate our model’s applicability by screening the DrugBank database, simulating practical toxicity screening scenarios in drug development.

Peptide drug development is currently receiving due attention as a modality between small and large molecules. Therapeutic peptides represent an opportunity to achieve high potency, selectivity, and reach intracellular targets. A new era in the development of therapeutic peptides emerged with the arrival of cyclic peptides which avoid the limitations of parenteral administration via achieving sufficient oral bioavailability. However, improving the membrane permeability of cyclic peptides remains one of the principal bottlenecks. Here, we introduce a deep learning regression model of cyclic peptide membrane permeability based on publicly available data. The model starts with a chemical structure and goes beyond the limited vocabulary language models to generalize to monomers beyond the ones in the training dataset. Moreover, we introduce an efficient estimator2generative wrapper to enable using the model in direct molecular optimization of membrane permeability via chemical modification. We name our application C2PO (Cyclic Peptide Permeability Optimizer). Lastly, we demonstrate how a molecule correction tool can be used to limit the presence of unfamiliar chemistry in the generated molecules.

Scientific contribution: We provide an ML-driven optimizer application, named C2PO, that returns structurally modified cyclic peptides with an improved membrane permeability, one of the pivotal tasks in drug discovery and development. C2PO is a first-in-class application for cyclic peptide permeability amelioration, in that it converts a ML model into a generative optimizer of chemical structures. Additionally, through demonstration we incentivize the usage of an automated post-correction tool with a chemistry reference library to correct strange chemistry outputs from C2PO, a known issue for ML-generated chemical structures.