Results 41 to 50 of about 12,257 (212)

Cheminformatics Analysis and Modeling with MacrolactoneDB [PDF]

Scientific Reports, 2020
AbstractMacrolactones, macrocyclic lactones with at least twelve atoms within the core ring, include diverse natural products such as macrolides with potent bioactivities (e.g. antibiotics) and useful drug-like characteristics. We have developed MacrolactoneDB, which integrates nearly 14,000 existing macrolactones and their bioactivity information from
Zin, Phyo Phyo Kyaw, Williams, Gavin J., Ekins, Sean   +2 more
openaire   +2 more sources

Environmental Detective Work: Environmental Cheminformatics and the Exposome [PDF]

, 2022
The multitude of chemicals to which we are exposed is ever increasing, with over 110 million chemicals in the largest open chemical databases, over 350,000 in global use inventories, and over 70,000 estimated to be in household use alone.
SCHYMANSKI, Emma
core   +1 more source

QUIMIOINFORMÁTICA: UMA INTRODUÇÃO

Química Nova
Cheminformatics is an interdisciplinary field between chemistry and informatics, which has evolved considerably since its inception in the 1960s. Initially, the cheminformatics community dealt primarily with practical and technical aspects of chemical ...
Vinicius M. Alves, Rodolpho C. Braga, Eugene N. Muratov, Carolina Horta Andrade   +3 more
doaj   +1 more source

Cheminformatics for Risk Assessment of Transformation Products [PDF]

, 2023
Cheminformatics analysis using RDKit of Transformation Products for Risk Assessment done in a Jupyter Notebook.
Adelene Lai
core   +1 more source

A Comprehensive 19F NMR Framework for Fragment‐Based Drug Discovery: The Validated Screening Library OpenFL600 and Efficient Affinity Ranking by CSAR

Angewandte Chemie, EarlyView.
NMR screening is a powerful method for hit detection in drug‐discovery. We designed and validated the OpenFL600 19F$^{19}{\rm F}$ NMR library to probe diverse targets, including RNA, GPCRs, kinases, and proteases. This library yields target‐specific ligands without generating promiscuous binders.
Simon H. Rüdisser, Gabriela Stadler, Giacomo Padroni, Daria Barbash, Marco M. Ruckstuhl, Nils Lorz, Christina Jordan, Sarah Skibinski, Timo T. Stühlinger, Sarah E. Kratzwald, Katharina M. Siess, Sven Brüschweiler, Gerald Platzer, Antoine Cléry, Robert Konrat, Frédéric H.‐.T. Allain, Alvar D. Gossert   +16 more
wiley   +2 more sources

Verifying the fully “Laplacianised” posterior Naïve Bayesian approach and more [PDF]

, 2015
Mussa and Glen would like to thank Unilever for financial support, whereas Mussa and Mitchell thank the BBSRC for funding this research through grant BB/I00596X/1.
Glen, Robert, Mitchell, JBO, Marcus, David, Glen, RC, Mussa, HY, Mussa, Hamse Yussuf, Marcus, D, Mitchell, John B. O.   +7 more
core   +1 more source

Human-Readable SMILES: Translating Cheminformatics to Chemistry [PDF]

, 2021
Molecular string representations are a key asset in cheminformatics and are becoming increasingly relevant to the general chemical community, due to the steadily growing impact of Big Data and Machine Learning.
Diego, Garay Ruiz, Diego, Garay-Ruiz, Carles, Bo   +2 more
core   +1 more source

Conformal prediction-based machine learning in Cheminformatics: Current applications and new challenges

Artificial Intelligence in the Life Sciences
Conformal Prediction (CP) is a distribution-free Machine Learning (ML) framework that has been developed in the last ∼25 years to provide well calibrated prediction subsets/intervals that include the true label with a user pre-defined probability, only ...
Mario Astigarraga, Andrés Sánchez-Ruiz, Gonzalo Colmenarejo   +2 more
doaj   +1 more source

Missed opportunities in large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery

Journal of Cheminformatics, 2019
Recently Bosc et al. (J Cheminform 11(1): 4, 2019), published an article describing a case study that directly compares conformal predictions with traditional QSAR methods for large-scale predictions of target-ligand binding. We consider this study to be
Damjan Krstajic
doaj   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source