Journal of Cheminformatics最新文献

The molecular volume, surface area, and polar molecular surface area are important descriptors for characterizing and predicting the molecular properties of lead compounds. Existing computational tools for calculating the above parameters often have complex workflows and are not well-suited for high-throughput conditions. CalVSP is an open-source software for computing molecular volume, molecular surface area, and polar surface area. The software implements a grid-based algorithm that dynamically optimizes grid spacing via quantum chemical reference data to ensure precise parameter calculations. CalVSP was tested on 9489 3D molecular structures, and the results revealed a mean absolute percentage error of 1.25% (95% CI: 1.23–1.27%) for the molecular volume and 1.33% (95% CI: 1.31–1.35%) for the molecular surface area compared with the quantum chemical data. For the molecular polar surface area calculations, the mean absolute percentage error was 4.59% (95% CI: 4.16–5.04%) across the 388 tested molecular structures. The CalVSP written in the C programming language offers a lightweight and easy tool. It can be integrated with other molecular property prediction tools to increase computational accuracy and for large-scale molecular calculations.

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Hundreds of computational methods for predicting ligand binding pockets exist, but the problem of finding druggable pockets throughout the human proteome persists. Different strategies for pocket-finding excel in different use cases. Ensemble models that leverage multiple different pocket-finding strategies can best capture diverse pockets at scale. Despite this, no publicly available human-proteome-wide datasets of pocket predictions from multiple pocket-finding methods exist. We present the Human Omnibus of Targetable Pockets (HOTPocket), a dataset of over 2.4 million predicted pockets over the entire human proteome that utilizes both experimentally-determined and computationally-predicted protein structures. We assembled this dataset by running seven diverse, established pocket-finding methods over all PDB and AlphaFold2 structures of the canonical human proteome. We created a novel pocket scoring method, hotpocketNN, which we used to filter candidate pockets and assemble the final proteome-wide dataset. Our hotpocketNN method is able to recover known ligand binding pockets, including those which are dissimilar from any pocket seen in its training set. The hotpocketNN method outperforms all constituent methods, including P2Rank and Fpocket, when assessing the precision with DCA criterion on the Astex Diverse Set and PoseBusters dataset. Additionally, hotpocketNN was able to identify recently-discovered druggable pockets on KRAS and the mu opioid receptor. We make both the HOTPocket dataset and the hotpocketNN method freely available.

Understanding molecular interactions beyond single-molecule properties is critical for studying real-world chemical systems. Quantum chemical calculations of molecule–molecule interactions are computationally demanding, making large, publicly available datasets scarce. Here, we present an efficient framework for generating initial configurations of molecular interaction systems and construct a molecular interaction dataset, containing 49,620 individual molecules and 247,741 molecular pairs spanning chromophore–solvent, solute–solvent, and drug–drug interactions, each associated with experimentally characterized equilibrium structures. Our dataset can be used for theoretical studies and machine learning applications in chemical sciences, particularly for modeling intermolecular interactions and structure-based prediction of experimental properties. In future work, we plan to expand the dataset to include non-equilibrium structures and atomic forces, thereby broadening its applicability to reaction modeling and force field development.

Peptide precursors, as the source molecules of bioactive peptides, play essential roles in neuroregulation, immune defense, and drug development. Their accurate identification is crucial for elucidating mechanisms of life regulation and developing novel therapeutics. However, the complexity and diversity of peptide precursor sequences pose significant challenges to prediction tasks. Existing methods predominantly rely on sequence features or structural features, hindering the full exploitation of complementary information between modalities and consequently limiting prediction performance. We introduce ProjFusNet, a deep learning framework that integrates evolutionary-scale protein sequence representations from ESM-2 with structural features via a projected multimodal fusion strategy. A bidirectional LSTM is further employed to model the complex interactions between sequence and structure. In rigorous five-fold cross-validation, ProjFusNet demonstrates improved performance across key metrics, including ACC, SN, AUC, SP, and MCC, compared to single-feature models.

The knowledge panels in PubChem allow users to quickly identify and summarize important relationships between chemicals, genes, proteins, and diseases by analyzing the co-occurrences of those entities in a collection of text documents. In the present study, the analysis and summarization techniques used to develop the literature knowledge panels in PubChem were extended to patent documents from the Google Patent Research Data (GPRD) set. The annotations of the patent documents in the GPRD set were mapped to NCBI database records to create the patent co-occurrence data. The annotations were not only from the titles and abstracts of patents but also from other parts such as claims and descriptions, greatly improving the coverage of the co-occurrence-based entity relationships in PubChem. Informativeness weights of entities were introduced in the co-occurrence and relevance score computations to account for a significant variation in the number of matched annotations per patent section. This narrows the focus to the co-occurrences that are more relevant to the subject matter of the patent. The resulting co-occurrence data was used to generate the patent knowledge panels, enabling users to identify entities co-mentioned in patents alongside a specific chemical or gene. The patent co-occurrence data can be downloaded interactively or accessed programmatically. Overall, the patent knowledge panels described in this study provide users with quick access to essential biomedical entities associated with a given PubChem record. Users can delve into relevant patent documents related to these entities or download the underlying co-occurrence data for further exploration and analysis.

Drug absorption can be altered due to the consumption of food, impacting the efficacy and safety of the drug administered, and predicting food effects (FE) can be quite complex. Traditional methods, including in vitro and in vivo models, fail to predict the full range of food-drug interactions owing to the biological variability of the gastrointestinal system. This review evaluates the predictive ability and accuracy of machine learning (ML) in predicting FE in comparison to conventional methods. We consider how ML models use food dataset information and assist in enhancing the formulation and dosing of the drugs. We discussed recent trends in FE prediction, its mechanisms, and effects on drug bioavailability. Supervised and unsupervised learning, as well as reinforcement learning, are analyzed in the context of absorption, distribution, metabolism, and elimination (ADME) forecasting and drug development. ML is certainly useful in addressing the issues posed by traditional methods; however, challenges about data quality, model generalizability, and integration into the drug development process are obstacles that must be overcome. This review explains how other emerging technologies, for example, PBPK modeling, can be combined with ML to enhance its prospects in the field of drug development. We examined prospects of deep learning, explainable artificial intelligence (AI), and ethical and legal aspects of applying ML in pharmacokinetics, as well as the interdisciplinary approaches that are required to improve patient care outcomes.

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