Journal of Chemical Information and Modeling 最新文献

As a model system, the binding pocket of the L99A mutant of T4 lysozyme has been the subject of numerous computational free energy studies. However, previous studies have failed to fully sample and account for the observed changes in the binding pocket of T4 L99A upon binding of a congeneric ligand series, limiting the accuracy of results. In this work, we resolve the closed, intermediate, and open states for T4 L99A previously reported in experiment in MD and establish definitions for these states based on the dynamics of the system. From this analysis, we arrive at two primary conclusions. First, assignment of simulation trajectories into discrete states should not be done simply based on RMSD to crystal structures as this can result in misassignment of states. Second, the different metastable conformations studied here need to be carefully treated, as we estimate the time scales for conformational interconversion to be on the order of 10² to 10³ ns─far longer than time scales for typical binding calculations. We conclude with a discussion on the need to develop enhanced sampling methods to generally account for significant changes in protein conformation due to relatively small ligand perturbations.

The increasing need to understand and control the environmental impact of chemical processes has revealed the challenge in efficient evaluation of toxicity of the vast number of chemical compounds and their varying effects on biological systems. In this study, we introduce "Build-a-bio-Strip", a novel online service designed to carry out a quick initial analysis of the toxic impact of chemical processes. This platform enables users to automatically generate toxicity characteristics of chemical reactions using their own data on cytotoxicity or median lethal doses of the substances involved or computational predictions based on SMILES strings. The service calculates the toxicity metrics such as bio-Factors and cytotoxicity potentials, which can be used to identify the substances with significant contributions to the overall toxicity of a particular process. This facilitates the selection of safer synthetic routes and the optimization of chemical processes from a toxicity perspective. "Build-a-bio-Strip" represents a step toward safer and more sustainable chemical practices. It is available free-of-charge at http://app.ananikovlab.ai:8080/.

The discovery of small organic compounds for inducing stem cell differentiation is a time- and resource-intensive process. While data science could, in principle, streamline the discovery of these compounds, novel approaches are required due to the difficulty of acquiring training data from large numbers of example compounds. In this paper, we present the design of a new compound for inducing cardiomyocyte differentiation using simple regression models trained with a data set containing only 80 examples. We introduce decorated shape descriptors, an information-rich molecular feature representation that integrates both molecular shape and hydrophilicity information. These models demonstrate improved performance compared to ones using standard molecular descriptors based on shape alone. Model overtraining is diagnosed using a new type of sensitivity analysis. Our new compound is designed using a conservative molecular design strategy, and its effectiveness is confirmed through expression profiles of cardiomyocyte-related marker genes using real-time polymerase chain reaction experiments on human iPS cell lines. This work demonstrates a viable data-driven strategy for designing new compounds for stem cell differentiation protocols and will be useful in situations where training data is limited.

The rapid progression of machine learning, especially deep learning (DL), has catalyzed a new era in drug discovery, introducing innovative approaches for predicting molecular properties. Despite the many methods available for feature representation, efficiently utilizing rich, high-dimensional information remains a significant challenge. Our work introduces ChemXTree, a novel graph-based model that integrates a Gate Modulation Feature Unit (GMFU) and neural decision tree (NDT) in the output layer to address this challenge. Extensive evaluations on benchmark data sets, including MoleculeNet and eight additional drug databases, have demonstrated ChemXTree's superior performance, surpassing or matching the current state-of-the-art models. Visualization techniques clearly demonstrate that ChemXTree significantly improves the separation between substrates and nonsubstrates in the latent space. In summary, ChemXTree demonstrates a promising approach for integrating advanced feature extraction with neural decision trees, offering significant improvements in predictive accuracy for drug discovery tasks and opening new avenues for optimizing molecular properties.

The μ-opioid receptor (MOR) is a G-protein coupled receptor involved in nociception and the primary target of opioid drugs. Understanding the relationships among the ligand structure, receptor dynamics, and efficacy in activating MOR is crucial for drug discovery and development. Here, we use coarse-grained normal-mode analysis to predict ligand-induced changes in receptor dynamics with the Quantitative Dynamics Activity Relationship (QDAR) DynaSig-ML methodology, training a LASSO regression model on the entropic signatures (ESs) computed from ligand-receptor complexes. We train and validate the methodology using a data set of 179 MOR ligands with experimentally measured efficacies split into strictly chemically different cross-validation sets. By analyzing the coefficients of the ES LASSO model, we identified key residues involved in MOR activation, several of which have mutational data supporting their role in MOR activation. Additionally, we explored a contact-only LASSO model based on ligand-protein interactions. While the model showed predictive power, it failed at predicting efficacy for ligands with low structural similarity to the training set, emphasizing the importance of receptor dynamics for predicting ligand-induced receptor activation. Moreover, the low computational cost of our approach, at 3 CPU s per ligand-receptor complex, opens the door to its application in large-scale virtual screening contexts. Our work contributes to a better understanding of dynamics-function relationships in the μ-opioid receptor and provides a framework for predicting ligand efficacy based on ligand-induced changes in receptor dynamics.

Peptaloid is the first dedicated database for peptide alkaloid molecules, a unique class of naturally derived compounds known for their structural diversity and significant biological activities. Despite their promising potential in drug discovery and therapeutic development, research on peptide alkaloids has been limited by the absence of a comprehensive and centralized resource. Fragmented data across various sources have posed a significant challenge, underscoring the need for a specialized database to facilitate more efficient research and application. Peptaloid addresses this critical gap by providing a database with over 161,000 peptide alkaloid entries, each detailed with structural, physicochemical, and pharmacological properties. By leveraging advanced computational tools and machine learning, Peptaloid generates ADMET profiles, aiding in identifying and optimizing therapeutic candidates. Designed for versatility, the database supports various applications beyond drug discovery, including ecology and material sciences. Peptaloid (as a specialized database for peptide alkaloids) will play a crucial role in innovation and collaboration across scientific disciplines. Peptaloid is accessible at https://peptaloid.niser.ac.in.

The dissociation rate constant (k_off) significantly impacts the drug potency and dosing frequency. This work proposes a powerful optimization-based framework for de novo drug design guided by k_off. First, a comprehensive database containing 2,773 unique k_off values is created. Based on the database, a novel generic dissociation kinetic model is developed with a mixture-of-experts architecture, enabling high-throughput predictions of k_off with high accuracy. The developed model is then integrated with an optimization-based mathematical programming approach to design drug candidates with low k_off. Finally, the τ-RAMD method is utilized to rigorously verify the designed potential drug candidates. In a case study, the framework successfully identified numerous new potential HSP90 inhibitor candidates, achieving a maximum 45.7% improvement in residence time (τ = 1/k_off) compared to that of a known exceptional HSP90 inhibitor. These findings demonstrate the feasibility and effectiveness of the kinetics-guided optimization-based de novo drug design framework in designing drug candidates with prolonged τ.

This study synergizes machine learning (ML) with conceptual density functional theory (CDFT) to develop OECD-compliant predictive models for the mutagenic activity of aromatic amines (AAs) with a fully No-Code methodology using a comprehensive data set of 251 AAs, Leave-One-Out-Cross-Validation (LOOCV), and three distinct data splits. Our research employs the GFN2-xTB method, known for its robustness and speed, to compute descriptors for procarcinogens and their activated metabolites in vacuum and aqueous phases. We evaluate the effectiveness of different theoretical definitions of electrophilicity within CDFT, namely, PSL, GCV, and CDP schemes, and the newly introduced Log QP descriptor to approximate Log P information. SPAARC, RandomTree, and JCHAID* ML methods were used to build explainable predictive models with highly robust internal validation (Avg. Correct Classifications = 76% and Avg. Kappa = 0.29) and external validation (Avg. Correct Classifications = 79% and Avg. Kappa = 0.33) metrics, and the results were compared to those of a two hidden layer Multilayer Perceptron. The results indicate that the second CDP definition for the electrophilicity in both vacuum and aqueous phases and also the newly presented Log QP descriptors are the most important ones for predicting the mutagenic activity of AA (namely ω_+Vac^CDP2+, ω_+Aq^CDP2+, and LogQP1_+Vac, respectively). The results indicate that metabolic activation, aqueous solvent properties, and the CDP electrophilicity schemes and Log QP should be considered when building predictive models for the mutagenic activity of AA. This study offers a replicable, No-Code approach to QSAR research, making high-level ML and CDFT applications accessible to a broader audience. Future work will expand these methods to other compound families, enhancing predictive capabilities in the study of mutagenic activities and other biological phenomena.

A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently available machine learning-based approaches. Here, we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root-mean-square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.