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1. GCLmf: A Novel Molecular Graph Contrastive Learning Framework Based on Hard Negatives and Application in Toxicity Prediction** NSTL国家科技图书文献中心

Xinxin Yu |  Yuanting Chen... -  《Molecular informatics.》 - 2025,44(1) - e202400169~e202400169 - 共13页

摘要:Abstract In silico methods for prediction of |  chemical toxicity can decrease the cost and increase the |  efficiency in the early stage of drug discovery. However | , due to low accessibility of sufficient and reliable |  toxicity data, constructing robust and accurate
关键词: chemical toxicity |  graph contrastive learning |  GNN |  hard negatives |  molecular representation |  self-supervised learning

2. Predicting the Price of Molecules Using Their Predicted Synthetic Pathways NSTL国家科技图书文献中心

Massina Abderrahmane |  Hamza Tajmouati... -  《Molecular informatics.》 - 2025,44(2) - e202400039~e202400039 - 共13页

摘要:Currently, numerous metrics allow chemists and |  computational chemists to refine and filter libraries of |  virtual molecules in order to prioritize their synthesis | . Some of the most commonly used metrics and models are |  QSAR models, docking scores, diverse druggability
关键词: deep learning; molecular price prediction; retrosynthesis; synthetic accessibility.

3. An Integrated Fuzzy Neural Network and Topological Data Analysis for Molecular Graph Representation Learning and Property Forecasting NSTL国家科技图书文献中心

Pham, Phu -  《Molecular informatics.》 - 2025,44(3) - e202400335~e202400335 - 共15页

摘要:Within a recent decade, graph neural network |  (GNN) has emerged as a powerful neural architecture |  for various graph-structured data modelling and task | -driven representation learning problems. Recent studies |  have highlighted the remarkable capabilities of GNNs
关键词: topological data analysis |  graph neural network |  neuro-fuzzy network |  molecular graph |  toxicity

4. CoLiNN: A Tool for Fast Chemical Space Visualization of Combinatorial Libraries Without Enumeration NSTL国家科技图书文献中心

Pikalyova, Regina |  Akhmetshin, Tagir... -  《Molecular informatics.》 - 2025,44(3) - e202400263~e202400263 - 共13页

摘要:Visualization of the combinatorial library |  chemical space provides a comprehensive overview of |  available compound classes, their diversity, and |  physicochemical property distribution - key factors in drug |  discovery. Typically, this visualization requires time
关键词: chemical space |  combinatorial library |  compound enumeration |  DNA-encoded libraries (DELs) |  GTM

5. Interpret Gaussian Process Models by Using Integrated Gradients NSTL国家科技图书文献中心

Fan Zhang |  Naoaki Ono... -  《Molecular informatics.》 - 2025,44(1) - e202400051~e202400051 - 共12页

摘要:Abstract Gaussian process regression (GPR) is |  a nonparametric probabilistic model capable of |  computing not only the predicted mean but also the |  predicted standard deviation, which represents the |  confidence level of predictions. It offers great
关键词: explainable AI |  gaussian process |  integrated gradients

6. Discovery of New HER2 Inhibitors via Computational Docking, Pharmacophore Modeling, and Machine Learning NSTL国家科技图书文献中心

Aseel Yasin Matrouk |  Haneen Mohammad... -  《Molecular informatics.》 - 2025,44(2) - e202400336~e202400336 - 共18页

摘要:The human epidermal growth factor receptor 2 |  (HER2) is a critical oncogene implicated in the |  development of various aggressive cancers, particularly |  breast cancer. Discovering novel HER2 inhibitors is |  crucial for expanding therapeutic options for HER2
关键词: Bagging; HER2; J48Graft; ML-QSAR; flexible docking.

7. Modeling Carbon Basicity NSTL国家科技图书文献中心

Fraczkiewicz, Robert |  Waldman, Marvin -  《Molecular informatics.》 - 2025,44(3) - e202400296~e202400296 - 共10页

摘要:This work presents a predictive model of |  aqueous ionization constants (pKa) of protonatable |  carbons in certain aromatic rings. The phenomenon of |  carbon atoms sometimes acting as a stable and |  reversible base accepting a proton in water solution is
关键词: carbon basicity |  ionization constant |  machine learning |  pKa |  prediction |  pK(a)

8. Active learning approaches in molecule pKi prediction NSTL国家科技图书文献中心

Kashafutdinova, I. M... |  Poyezzhayeva, A.... -  《Molecular informatics.》 - 2025,44(1) - e202400154~e202400154 - 共14页

摘要:During the early stages of drug design | , identifying compounds with suitable bioactivities is crucial | . Given the vast array of potential drug databases, it |  ' s feasible to assay only a limited subset of |  candidates. The optimal method for selecting the candidates
关键词: active learning |  bioactivity |  ChEMBL datasets

9. MAYA (Multiple ActivitY Analyzer): An Open Access Tool to Explore Structure-Multiple Activity Relationships in the Chemical Universe NSTL国家科技图书文献中心

J. Israel Espinoza-C... |  José L. Medina-Franc... -  《Molecular informatics.》 - 2025,44(2) - e202400306~e202400306 - 共7页

摘要:Herein, we introduce MAYA (Multiple Activity |  Analyzer), a tool designed to automatically construct a |  chemical multiverse, generating multiple visualizations |  of chemical spaces of a compound data set described |  by structural descriptors of different nature such
关键词: automatic data visualization; chemical multiverse; chemical space; chemoinformatics; open

10. Deep Modeling of Gain-of-Function Mutations on Androgen Receptor NSTL国家科技图书文献中心

You, Jiaying |  Foo, Jane... -  《Molecular informatics.》 - 2025,44(4) - e202500018~e202500018 - 共10页

摘要:The efficiency of Androgen Receptor (AR | ) pathway inhibitors for prostate cancer (PCa) is on |  decline due to resistance mechanisms including the |  occurrence of gain-of-function mutations on human androgen |  receptor (AR). Hence, understanding and predicting such
关键词: Machine learning |  AR |  Prostate Cancer
检索条件出处:Molecular informatics.
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