Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure–Activity Relationships [PDF]
Molecules, 2023 A deep learning-based quantitative structure–activity relationship analysis, namely the molecular image-based DeepSNAP–deep learning method, can successfully and automatically capture the spatial and temporal features in an image generated from a three ...
Yasunari Matsuzaka, Yoshihiro Uesawa
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Mordred: a molecular descriptor calculator [PDF]
Journal of Cheminformatics, 2018 Molecular descriptors are widely employed to present molecular characteristics in cheminformatics. Various molecular-descriptor-calculation software programs have been developed.
Hirotomo Moriwaki +3 more
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A molecular descriptor of intramolecular noncovalent interaction for regulating optoelectronic properties of organic semiconductors. [PDF]
Nat Commun, 2023 Intramolecular noncovalent interactions can block molecules in a given conformation enhancing performance of organic semiconductors. Here, the authors report a molecular descriptor to weigh them that strongly correlates with the reorganization energy of ...
Liu M, Han X, Chen H, Peng Q, Huang H.
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A New Graph-Based Molecular Descriptor Using the Canonical Representation of the Molecule [PDF]
The Scientific World Journal, 2014 Molecular similarity is a pervasive concept in drug design. The basic idea underlying molecular similarity is the similar property principle, which states that structurally similar molecules will exhibit similar physicochemical and biological properties.
Hamza Hentabli +3 more
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Prediction of Blood-Brain Barrier Penetration (BBBP) Based on Molecular Descriptors of the Free-Form and In-Blood-Form Datasets [PDF]
Molecules, 2021 The blood-brain barrier (BBB) controls the entry of chemicals from the blood to the brain. Since brain drugs need to penetrate the BBB, rapid and reliable prediction of BBB penetration (BBBP) is helpful for drug development.
Hiroshi Sakiyama +2 more
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Utilizing Molecular Descriptor Importance to Enhance Endpoint Predictions [PDF]
ToxicsQuantitative structure–activity relationship (QSAR) models are essential for predicting endpoints that are otherwise challenging to estimate using other in silico approaches.
Benjamin Bajželj +2 more
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Harnessing Shannon entropy-based descriptors in machine learning models to enhance the prediction accuracy of molecular properties [PDF]
Journal of Cheminformatics, 2023 Accurate prediction of molecular properties is essential in the screening and development of drug molecules and other functional materials. Traditionally, property-specific molecular descriptors are used in machine learning models.
Rajarshi Guha, Darrell Velegol
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A comparative study of machine learning models on molecular fingerprints for odor decoding [PDF]
Communications ChemistryUnderstanding how molecular structure relates to odor perception is a longstanding problem, with important implications for fragrance development and sensory science.
Jinyoung Suh, Yeonju Hong, Chunho Park
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Collision Cross Section Prediction Based on Machine Learning
Molecules, 2023 Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs.
Xiaohang Li +8 more
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Biomolecules, 2021 Malaria remains by far one of the most threatening and dangerous illnesses caused by the plasmodium falciparum parasite. Chloroquine (CQ) and first-line artemisinin-based combination treatment (ACT) have long been the drug of choice for the treatment and
Medard Edmund Mswahili +4 more
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