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Quantitative Structure-Activity Relationship

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Application of machine learning-based read-across structure-property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye-sensitized solar cells (DSSCs).

Molecular informatics
The application of various in-silico-based approaches for the prediction of various properties of materials has been an effective alternative to experimental methods. Recently, the concepts of Quantitative structure-property relationship (QSPR) and r...

Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects.

Molecular pharmaceutics
Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that relate the molecular structure to compound properties. Such quantitative structure-property relationship models are generally trained on large data sets ...

Quantitative structure-property relationship modelling on autoignition temperature: evaluation and comparative analysis.

SAR and QSAR in environmental research
The autoignition temperature (AIT) serves as a crucial indicator for assessing the potential hazards associated with a chemical substance. In order to gain deeper insights into model performance and facilitate the establishment of effective methodolo...

Development of a standardized methodology for transfer learning with QSAR models: a purely data-driven approach for source task selection.

SAR and QSAR in environmental research
Transfer learning is a machine learning technique that works well with chemical endpoints, with several papers confirming its efficiency. Although effective, because the choice of source/assistant tasks is non-trivial, the application of this techniq...

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview.

Journal of chemical information and modeling
Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity a...

Descriptor generation from Morgan fingerprint using persistent homology.

SAR and QSAR in environmental research
In cheminformatics, molecular fingerprints (FPs) are used in various tasks such as regression and classification. However, predictive models often underutilize Morgan FP for regression and related tasks in machine learning. This study introduced desc...

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Biomolecules
Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the va...

Investigating the bispecific lead compounds against methicillin-resistant SarA and CrtM using machine learning and molecular dynamics approach.

Journal of biomolecular structure & dynamics
Methicillin-resistant Staphylococcus aureus (MRSA) is a notorious pathogen that has emerged as a serious global health concern over the past few decades. Staphylococcal accessory regulator A (SarA) and 4,4'-diapophytoene synthase (CrtM) play a crucia...

Integrating machine learning and high throughput screening for the discovery of allosteric AKT1 inhibitors.

Journal of biomolecular structure & dynamics
Evidence from clinical and experimental investigations reveals the role of AKT in oral cancer, which has led to the development of therapeutic and pharmacological medications for inhibiting AKT protein. Despite prodigious effort, researchers are sear...

Advancing chronic toxicity risk assessment in freshwater ecology by molecular characterization-based machine learning.

Environmental pollution (Barking, Essex : 1987)
The continuously increased production of various chemicals and their release into environments have raised potential negative effects on ecological health. However, traditional labor-intensive assessment methods cannot effectively and rapidly evaluat...