Results 1 to 10 of about 14,133 (217)

School of cheminformatics in Latin America

Journal of Cheminformatics, 2023
We report the major highlights of the School of Cheminformatics in Latin America, Mexico City, November 24–25, 2022. Six lectures, one workshop, and one roundtable with four editors were presented during an online public event with speakers from academia,
Karla Gonzalez-Ponce, Carolina Horta Andrade, Fiona Hunter, Johannes Kirchmair, Karina Martinez-Mayorga, José L. Medina-Franco, Matthias Rarey, Alexander Tropsha, Alexandre Varnek, Barbara Zdrazil   +9 more
doaj   +8 more sources

Utilizing Molecular Descriptor Importance to Enhance Endpoint Predictions

Toxics
Quantitative structure–activity relationship (QSAR) models are essential for predicting endpoints that are otherwise challenging to estimate using other in silico approaches.
Benjamin Bajželj, Marjana Novič, Viktor Drgan   +2 more
doaj   +1 more source

In Silico Studies of Lamiaceae Diterpenes with Bioinsecticide Potential against Aphis gossypii and Drosophila melanogaster

Molecules, 2021
Background: The growing demand for agricultural products has led to the misuse/overuse of insecticides; resulting in the use of higher concentrations and the need for ever more toxic products. Ecologically, bioinsecticides are considered better and safer
Gabriela Cristina Soares Rodrigues, Mayara dos Santos Maia, Andreza Barbosa Silva Cavalcanti, Natália Ferreira de Sousa, Marcus Tullius Scotti, Luciana Scotti   +5 more
doaj   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

An In Silico Approach for Assessment of the Membrane Transporter Activities of Phenols: A Case Study Based on Computational Models of Transport Activity for the Transporter Bilitranslocase

Molecules, 2019
Phenols are the most abundant naturally accessible antioxidants present in a human normal diet. Since numerous beneficial applications of phenols as preventive agents in various diseases were revealed, the evaluation of phenols bioavailability is of high
Katja Venko, Marjana Novič
doaj   +1 more source

Embedded Morgan Fingerprints for More Efficient Molecular Property Predictions With Machine Learning

Advanced Intelligent Discovery, EarlyView.
High dimensionality of Morgan fingerprints causes overfitting and slow training. Embedded Morgan Fingerprint (eMFP) compresses this encoding while keeping key structural information intact. Tested on five regression models across three databases for HOMO–LUMO gap prediction, eMFP outperforms standard fingerprints, training faster on large, high ...
Emilio Nuñez‐Andrade, Isaac Vidal‐Daza, Rafael Gómez‐Bombarelli, James W. Ryan, Francisco J. Martin‐Martinez   +4 more
wiley   +1 more source

Unveiling the Potent Antiviral and Antioxidant Activities of an Aqueous Extract from Caesalpinia mimosoides Lamk: Cheminformatics and Molecular Docking Approaches

Foods, 2023
Our group previously demonstrated that Caesalpinia mimosoides Lamk exhibits many profound biological properties, including anticancer, antibacterial, and antioxidant activities. However, its antiviral activity has not yet been investigated.
Anuwatchakij Klamrak, Jaran Nabnueangsap, Jaraspim Narkpuk, Yutthakan Saengkun, Piyapon Janpan, Napapuch Nopkuesuk, Arunrat Chaveerach, Samaporn Teeravechyan, Shaikh Shahinur Rahman, Theerawat Dobutr, Poramet Sitthiwong, Pornsuda Maraming, Natsajee Nualkaew, Nisachon Jangpromma, Rina Patramanon, Sakda Daduang, Jureerut Daduang   +16 more
doaj   +1 more source

MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design

Advanced Intelligent Discovery, EarlyView.
MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa, Tejs Vegge, Jes Frellsen   +2 more
wiley   +1 more source

Bond Energy Descriptors Enable Machine Learning With Limited Data: Design of Capping Agents for Thermoplastic Polyurethane Recycling

Advanced Intelligent Discovery, EarlyView.
Data scarcity is a critical bottleneck for machine learning in applied chemistry. We show that physics‐informed descriptors enable a simple linear model to predict the deblocking temperatures of capped diisocyanates to within 11 °C from just 19 measurements, demonstrating that embedding physical insight into descriptors, rather than relying on ...
Remsha Rafiq, Barbaro Zulueta, Hannah Zucco, Ramakrishna Suresh, Jason E. Shoemaker, Michael Call, Daylan Sheppard, Glenn Cormack, John A. Keith, Götz Veser   +9 more
wiley   +1 more source

CDK-Taverna: an open workflow environment for cheminformatics

BMC Bioinformatics, 2010
Background Small molecules are of increasing interest for bioinformatics in areas such as metabolomics and drug discovery. The recent release of large open access chemistry databases generates a demand for flexible tools to process them and discover new ...
Zielesny Achim, Willighagen Egon L, Kuhn Thomas, Steinbeck Christoph   +3 more
doaj   +1 more source