Molecular Informatics最新文献_第9页

In silico prediction of drug-induced liver injury with a complementary integration strategy based on hybrid representation. 基于混合表示的互补整合策略的药物性肝损伤的计算机预测。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-07-01 DOI: 10.1002/minf.202200284

Yaxin Gu, Yimeng Wang, Zengrui Wu, Weihua Li, Guixia Liu, Yun Tang

{"title":"In silico prediction of drug-induced liver injury with a complementary integration strategy based on hybrid representation.","authors":"Yaxin Gu, Yimeng Wang, Zengrui Wu, Weihua Li, Guixia Liu, Yun Tang","doi":"10.1002/minf.202200284","DOIUrl":"https://doi.org/10.1002/minf.202200284","url":null,"abstract":"Drug-induced liver injury (DILI) is one of the major causes of drug withdrawals, acute liver injury and blackbox warnings. Clinical diagnosis of DILI is a huge challenge due to the complex pathogenesis and lack of specific biomarkers. In recent years, machine learning methods have been used for DILI risk assessment, but the model generalization does not perform satisfactorily. In this study, we constructed a large DILI data set and proposed an integration strategy based on hybrid representations for DILI prediction (HR-DILI). Benefited from feature integration, the hybrid graph neural network models outperformed single representation-based models, among which hybrid-GraphSAGE showed balanced performance in cross-validation with AUC (area under the curve) as 0.804±0.019. In the external validation set, HR-DILI improved the AUC by 6.4 %-35.9 % compared to the base model with a single representation. Compared with published DILI prediction models, HR-DILI had better and balanced performance. The performance of local models for natural products and synthetic compounds were also explored. Furthermore, eight key descriptors and six structural alerts associated with DILI were analyzed to increase the interpretability of the models. The improved performance of HR-DILI indicated that it would provide reliable guidance for DILI risk assessment.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9849638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring activity landscapes with extended similarity: is Tanimoto enough? 探索具有扩展相似性的活动景观:谷本是否足够?

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-07-01 DOI: 10.1002/minf.202300056

Timothy B Dunn, Edgar López-López, Taewon David Kim, José L Medina-Franco, Ramón Alain Miranda-Quintana

{"title":"Exploring activity landscapes with extended similarity: is Tanimoto enough?","authors":"Timothy B Dunn, Edgar López-López, Taewon David Kim, José L Medina-Franco, Ramón Alain Miranda-Quintana","doi":"10.1002/minf.202300056","DOIUrl":"https://doi.org/10.1002/minf.202300056","url":null,"abstract":"Understanding structure-activity landscapes is essential in drug discovery. Similarly, it has been shown that the presence of activity cliffs in compound data sets can have a substantial impact not only on the design progress but also can influence the predictive ability of machine learning models. With the continued expansion of the chemical space and the currently available large and ultra-large libraries, it is imperative to implement efficient tools to analyze the activity landscape of compound data sets rapidly. The goal of this study is to show the applicability of the n-ary indices to quantify the structure-activity landscapes of large compound data sets using different types of structural representation rapidly and efficiently. We also discuss how a recently introduced medoid algorithm provides the foundation to finding optimum correlations between similarity measures and structure-activity rankings. The applicability of the n-ary indices and the medoid algorithm is shown by analyzing the activity landscape of 10 compound data sets with pharmaceutical relevance using three fingerprints of different designs, 16 extended similarity indices, and 11 coincidence thresholds.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9794062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Identification of a PD1/PD-L1 inhibitor by structure-based pharmacophore modelling, virtual screening, molecular docking and biological evaluation. 基于结构的药效团建模、虚拟筛选、分子对接和生物学评价鉴定PD1/PD-L1抑制剂。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-06-01 DOI: 10.1002/minf.202200254

Gopi Mohan C, Anju Pushkaran, Kumaran K, Ann MariaT, Raja Biswas

引用次数: 1

Compression of molecular fingerprints with autoencoder networks. 用自编码器网络压缩分子指纹。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-06-01 DOI: 10.1002/minf.202300059

Gisbert Schneider, Agnieszka Ilnicka

引用次数: 2

Overproduce and select, or determine optimal molecular descriptor subset via configuration space optimization? Application to the prediction of ecotoxicological endpoints. 过度生产和选择，还是通过构型空间优化确定最佳分子描述子子集?生态毒理学终点预测的应用。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-06-01 DOI: 10.1002/minf.202200227

Luis A García-González, Yovani Marrero-Ponce, Carlos A Brizuela, César R García-Jacas

{"title":"Overproduce and select, or determine optimal molecular descriptor subset via configuration space optimization? Application to the prediction of ecotoxicological endpoints.","authors":"Luis A García-González, Yovani Marrero-Ponce, Carlos A Brizuela, César R García-Jacas","doi":"10.1002/minf.202200227","DOIUrl":"https://doi.org/10.1002/minf.202200227","url":null,"abstract":"Predicting the likely biological activity (or property) of compounds is a fundamental and challenging task in the drug discovery process. Current computational methodologies aim to improve their predictive accuracies by using deep learning (DL) approaches. However, non-DL based approaches for small- and medium-sized chemical datasets have demonstrated to be most suitable for. In this approach, an initial universe of molecular descriptors (MDs) is first calculated, then different feature selection algorithms are applied, and finally, one or several predictive models are built. Herein we demonstrate that this traditional approach may miss relevant information by assuming that the initial universe of MDs codifies all relevant aspects for the respective learning task. We argue that this limitation is mainly because of the constrained intervals of the parameters used in the algorithms that compute MDs, parameters that define the Descriptor Configuration Space (DCS). We propose to relax these constraints in an open CDS approach, so that a larger universe of MDs can be initially considered. We model the generation of MDs as a multicriteria optimization problem and tackle it with a variant of the standard genetic algorithm. As a novel component, the fitness function is computed by aggregating four criteria via the Choquet integral. Experimental results show that the proposed approach generates a meaningful DCS by improving state-of-the-art approaches in most of the benchmarking chemical datasets accounted for.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9682498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Gas-to-ionic liquid partition: QSPR modeling and mechanistic interpretation. 气体-离子液体分配:QSPR模型和机理解释。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-06-01 DOI: 10.1002/minf.202200223

Jia-Xi Chang, Jian-Wei Zou, Chao-Yuan Lou, Jia-Xin Ye, Rui Feng, Zi-Yuan Li, Gui-Xiang Hu

{"title":"Gas-to-ionic liquid partition: QSPR modeling and mechanistic interpretation.","authors":"Jia-Xi Chang, Jian-Wei Zou, Chao-Yuan Lou, Jia-Xin Ye, Rui Feng, Zi-Yuan Li, Gui-Xiang Hu","doi":"10.1002/minf.202200223","DOIUrl":"https://doi.org/10.1002/minf.202200223","url":null,"abstract":"The present work was devoted to explore the quantitative structure-property relationships for gas-to-ionic liquid partition coefficients (log KILA ). A series of linear models were first established for the representative dataset (IL01). The optimal model was a four-parameter equation (1Ed) consisting of two electrostatic potential-based descriptors ( <math> <semantics><mrow><mi>Σ</mi> <msubsup><mi>V</mi> <mrow><mi>s</mi> <mo>,</mo> <mi>i</mi> <mi>n</mi> <mi>d</mi></mrow> <mo>-</mo></msubsup> </mrow> <annotation>${{rm { Sigma }}{V}_{s,ind}^{-}}$</annotation> </semantics> </math> and Vs,max ), one 2D matrix-based descriptor (J_D/Dt) and dipole moment (μ). All of the four descriptors introduced in the model can find the corresponding parameters, directly or indirectly, from Abraham's linear solvation energy relationship (LSER) or its theoretical alternatives, which endows the model good interpretability. Gaussian process was utilized to build the nonlinear model. Systematical validations, including 5-fold cross-validation for the training set, the validation for test set, as well as a more rigorous Monte Carlo cross-validation were performed to verify the reliability of the constructed models. Applicability domain of the model was evaluated, and the Williams plot revealed that the model can be used to predict the log KILA values of structurally diverse solutes. The other 13 datasets were also processed in the same way, and all of the linear models with expressions similar to equation 1Ed were obtained. These models, whether linear of nonlinear, represent satisfactory statistical results, which confirms the universality of the method adopted in this study in QSPR modeling of gas-to-IL partition.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10056650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Augmenting bioactivity by docking-generated multiple ligand poses to enhance machine learning and pharmacophore modelling: discovery of new TTK inhibitors as case study. 通过对接产生的多个配体姿势来增强生物活性，以增强机器学习和药效团建模:发现新的TTK抑制剂作为案例研究。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-06-01 DOI: 10.1002/minf.202300022

Amenah M Al-Imam, Safa Daoud, Ma'mon M Hatmal, Mutasem Omar Taha

{"title":"Augmenting bioactivity by docking-generated multiple ligand poses to enhance machine learning and pharmacophore modelling: discovery of new TTK inhibitors as case study.","authors":"Amenah M Al-Imam, Safa Daoud, Ma'mon M Hatmal, Mutasem Omar Taha","doi":"10.1002/minf.202300022","DOIUrl":"https://doi.org/10.1002/minf.202300022","url":null,"abstract":"Dual specificity protein kinase threonine/Tyrosine kinase (TTK) is one of the mitotic kinases. High levels of TTK are detected in several types of cancer. Hence, TTK inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of TTK inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contacts Fingerprints and docking scoring values were used as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to determine critical descriptors for predicting anti-TTK bioactivity and for pharmacophore generation. Three successful pharmacophores were deduced and subsequently used for in silico screening against the NCI database. A total of 14 hits were evaluated in vitro for their anti-TTK bioactivities. One hit of novel chemotype showed reasonable dose-response curve with experimental IC50 of 1.0 μM. The presented work indicates the validity of data augmentation using multiple docked poses for building successful machine learning models and pharmacophore hypotheses.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9675061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Co-model for chemical toxicity prediction based on multi-task deep learning. 基于多任务深度学习的化学毒性预测协同模型。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-05-01 DOI: 10.1002/minf.202200257

Yuan Yuan Li, Lingfeng Chen, Chengtao Pu, Chengdong Zang, YingChao Yan, Yadong Chen, Yanmin Zhang, Haichun Liu

{"title":"Co-model for chemical toxicity prediction based on multi-task deep learning.","authors":"Yuan Yuan Li, Lingfeng Chen, Chengtao Pu, Chengdong Zang, YingChao Yan, Yadong Chen, Yanmin Zhang, Haichun Liu","doi":"10.1002/minf.202200257","DOIUrl":"https://doi.org/10.1002/minf.202200257","url":null,"abstract":"The toxicity of compounds is closely related to the effectiveness and safety of drug development, and accurately predicting the toxicity of compounds is one of the most challenging tasks in medicinal chemistry and pharmacology. In this paper, we construct three types of models for single and multi-tasking based on 2D and 3D descriptors, fingerprints and molecular graphs, and then validate the models with benchmark tests on the Tox21 data challenge. We found that due to the information sharing mechanism of multi-task learning, it could address the imbalance problem of the Tox21 data sets to some extent, and the prediction performance of the multi-task was significantly improved compared with the single task in general. Given the complement of the different molecular representations and modeling algorithms, we attempted to integrate them into a robust Co-Model. Our Co-Model performs well in various evaluation metrics on the test set and also achieves significant performance improvement compared to other models in the literature, which clearly demonstrates its superior predictive power and robustness.","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9510308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring cooperative molecular contacts using a PostgreSQL database system. 利用PostgreSQL数据库系统探索合作分子接触。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-05-01 DOI: 10.1002/minf.202200235

Mael A Briand, Loïc Dreano, Ashenafi Legehar, Evgeni Grazhdankin, Leo Ghemtio, Henri Xhaard

引用次数: 0

A machine learning strategy with clustering under sampling of majority instances for predicting drug target interactions. 预测药物靶标相互作用的多数实例下聚类机器学习策略。

IF 3.6 4区医学

Molecular Informatics Pub Date : 2023-05-01 DOI: 10.1002/minf.202200102

Tanya Liyaqat, Tanvir Ahmad

引用次数: 0