Correction for 'QSAR without borders' by Eugene N. Muratov et al., Chem. Soc. Rev., 2020, DOI: 10.1039/d0cs00098a.
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Ionization energies (IEs) of organic compounds come in different forms-adiabatic, vertical, as electrode potentials, or as orbital eigenvalues Koopmans' theorem. They have been linked to the reactivity towards electrophiles and have been used to quantitatively describe electron transfer processes. The prediction of IEs is only meaningful when an estimate of the prediction's uncertainty is included.
View Article and Find Full Text PDFSynergizing Machine Learning, Conceptual Density Functional Theory, and Biochemistry: No-Code Explainable Predictive Models for Mutagenicity in Aromatic Amines.
J Chem Inf Model
November 2024
Departamento de Investigación y Desarrollo, ConsultoresAcademicos SpA, Santiago 1137, Santiago 8340457, Chile.
This study synergizes machine learning (ML) with conceptual density functional theory (CDFT) to develop OECD-compliant predictive models for the mutagenic activity of aromatic amines (AAs) with a fully No-Code methodology using a comprehensive data set of 251 AAs, Leave-One-Out-Cross-Validation (LOOCV), and three distinct data splits. Our research employs the GFN2-xTB method, known for its robustness and speed, to compute descriptors for procarcinogens and their activated metabolites in vacuum and aqueous phases. We evaluate the effectiveness of different theoretical definitions of electrophilicity within CDFT, namely, PSL, GCV, and CDP schemes, and the newly introduced Log QP descriptor to approximate Log P information.
View Article and Find Full Text PDFCorrigendum: Deepening insights into cholinergic agents for intraocular pressure reduction: systems genetics, molecular modeling, and perspectives.
Front Mol Biosci
October 2024
Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, United States.
[This corrects the article DOI: 10.3389/fmolb.2024.
View Article and Find Full Text PDFAutomated Workflows for Data Curation and Machine Learning to Develop Quantitative Structure-Activity Relationships.
Methods Mol Biol
September 2024
Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
The recent advancements in machine learning and the new availability of large chemical datasets made the development of tools and protocols for computational chemistry a topic of high interest. In this chapter a standard procedure to develop Quantitative Structure-Activity Relationship (QSAR) models was presented and implemented in two freely available and easy-to-use workflows. The first workflow helps the user retrieving chemical data (SMILES) from the web, checking their correctness and curating them to produce consistent and ready-to-use datasets for cheminformatic.
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