PyDPI: Freely Available Python Package for Chemoinformatics, Bioinformatics, and Chemogenomics Studies

The rapidly increasing amount of publicly available data in biology and chemistry enables researchers to revisit interaction problems by systematic integration and analysis of heterogeneous data. Herein, we developed a comprehensive python package to emphasize the integration of chemoinformatics and bioinformatics into a molecular informatics platform for drug discovery. PyDPI (drug–protein interaction with Python) is a powerful python toolkit for computing commonly used structural and physicochemical features of proteins and peptides from amino acid sequences, molecular descriptors of drug molecules from their topology, and protein–protein interaction and protein–ligand interaction descriptors. It computes 6 protein feature groups composed of 14 features that include 52 descriptor types and 9890 descriptors, 9 drug feature groups composed of 13 descriptor types that include 615 descriptors. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pair fingerprints, topological torsion fingerprints, and Morgan/circular fingerprints. By combining different types of descriptors from drugs and proteins in different ways, interaction descriptors representing protein–protein or drug–protein interactions could be conveniently generated. These computed descriptors can be widely used in various fields relevant to chemoinformatics, bioinformatics, and chemogenomics. PyDPI is freely available via https://sourceforge.net/projects/pydpicao/

Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches

Hematotoxicity has been becoming a serious but overlooked toxicity in drug discovery. However, only a few in silico models have been reported for the prediction of hematotoxicity. In this study, we constructed a high-quality dataset comprising 759 hematotoxic compounds and 1623 nonhematotoxic compounds and then established a series of classification models based on a combination of seven machine learning (ML) algorithms and nine molecular representations. The results based on two data partitioning strategies and applicability domain (AD) analysis illustrate that the best prediction model based on Attentive FP yielded a balanced accuracy (BA) of 72.6%, an area under the receiver operating characteristic curve (AUC) value of 76.8% for the validation set, and a BA of 69.2%, an AUC of 75.9% for the test set. In addition, compared with existing filtering rules and models, our model achieved the highest BA value of 67.5% for the external validation set. Additionally, the shapley additive explanation (SHAP) and atom heatmap approaches were utilized to discover the important features and structural fragments related to hematotoxicity, which could offer helpful tips to detect undesired positive substances. Furthermore, matched molecular pair analysis (MMPA) and representative substructure derivation technique were employed to further characterize and investigate the transformation principles and distinctive structural features of hematotoxic chemicals. We believe that the novel graph-based deep learning algorithms and insightful interpretation presented in this study can be used as a trustworthy and effective tool to assess hematotoxicity in the development of new drugs

MOESM1 of BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions

Additional file 1. BioChem features

Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches

Hematotoxicity has been becoming a serious but overlooked toxicity in drug discovery. However, only a few in silico models have been reported for the prediction of hematotoxicity. In this study, we constructed a high-quality dataset comprising 759 hematotoxic compounds and 1623 nonhematotoxic compounds and then established a series of classification models based on a combination of seven machine learning (ML) algorithms and nine molecular representations. The results based on two data partitioning strategies and applicability domain (AD) analysis illustrate that the best prediction model based on Attentive FP yielded a balanced accuracy (BA) of 72.6%, an area under the receiver operating characteristic curve (AUC) value of 76.8% for the validation set, and a BA of 69.2%, an AUC of 75.9% for the test set. In addition, compared with existing filtering rules and models, our model achieved the highest BA value of 67.5% for the external validation set. Additionally, the shapley additive explanation (SHAP) and atom heatmap approaches were utilized to discover the important features and structural fragments related to hematotoxicity, which could offer helpful tips to detect undesired positive substances. Furthermore, matched molecular pair analysis (MMPA) and representative substructure derivation technique were employed to further characterize and investigate the transformation principles and distinctive structural features of hematotoxic chemicals. We believe that the novel graph-based deep learning algorithms and insightful interpretation presented in this study can be used as a trustworthy and effective tool to assess hematotoxicity in the development of new drugs

MOESM2 of BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions

Additional file 2. BioProt features

MOESM3 of BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions

Additional file 3. BioDNA features

Mallotus paniculatus Muell.-Arg.

原著和名: [記載なし]科名: トウダイグサ科 = Euphorbiaceae採集地: タイ チャンタブリ (タイ国 チャンタブリ)採集日:採集者: 萩庭丈壽整理番号: JH051919国立科学博物館整理番号: TNS-VS-94934

ADME Properties Evaluation in Drug Discovery: Prediction of Caco‑2 Cell Permeability Using a Combination of NSGA-II and Boosting

The Caco-2 cell monolayer model is a popular surrogate in predicting the <i>in vitro</i> human intestinal permeability of a drug due to its morphological and functional similarity with human enterocytes. A quantitative structure–property relationship (QSPR) study was carried out to predict Caco-2 cell permeability of a large data set consisting of 1272 compounds. Four different methods including multivariate linear regression (MLR), partial least-squares (PLS), support vector machine (SVM) regression and Boosting were employed to build prediction models with 30 molecular descriptors selected by nondominated sorting genetic algorithm-II (NSGA-II). The best Boosting model was obtained finally with <i>R</i>² = 0.97, RMSE_F = 0.12, <i>Q</i>² = 0.83, RMSE_CV = 0.31 for the training set and <i>R</i>_T² = 0.81, RMSE_T = 0.31 for the test set. A series of validation methods were used to assess the robustness and predictive ability of our model according to the OECD principles and then define its applicability domain. Compared with the reported QSAR/QSPR models about Caco-2 cell permeability, our model exhibits certain advantage in database size and prediction accuracy to some extent. Finally, we found that the polar volume, the hydrogen bond donor, the surface area and some other descriptors can influence the Caco-2 permeability to some extent. These results suggest that the proposed model is a good tool for predicting the permeability of drug candidates and to perform virtual screening in the early stage of drug development

MOESM2 of ChemSAR: an online pipelining platform for molecular SAR modeling

Additional file 2: Table S1. Classification results of different models in the evaluation of Caco-2 Cell permeability. Fig. S1. The ROC curves for different models in the evaluation of Caco-2 Cell permeability

The predictive probability plot of screening all cross-linking drug-target pairs. The size of predictive probability gradually varies from green to red.

<p>The predictive probability plot of screening all cross-linking drug-target pairs. The size of predictive probability gradually varies from green to red.</p