Results 31 to 40 of about 21,282 (192)

SBE13, a newly identified inhibitor of inactive polo-like kinase 1 [PDF]

, 2010
Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 Protein kinases are important targets for drug development.
B Spänkuch, Ewgenij Proschak, Gisbert Schneider, S Keppner, Sarah Keppner, Y Liu   +5 more
core   +3 more sources

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   +1 more source

Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences

, 2013
Recently, a number of organisations have called for open access to scientific information and especially to the data obtained from publicly funded research, among which the Royal Society report and the European Commission press release are particularly ...
Bird, Colin, Frey, Jeremy G.
core   +1 more source

TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data [PDF]

, 2019
Owing to the increase in freely available software and data for cheminformatics and structural bioinformatics, research for computer-aided drug design (CADD) is more and more built on modular, reproducible, and easy-to-share pipelines.
Driller, Maximilian, Morger, Andrea, Sydow, Dominique, Volkamer, Andrea   +3 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

Rapid prediction of NMR spectral properties with quantified uncertainty [PDF]

, 2018
open access articleAccurate calculation of specific spectral properties for NMR is an important step for molecular structure elucidation. Here we report the development of a novel machine learning technique for accurately predicting chemical shifts of ...
Jonas, Eric, Kuhn, Stefan
core   +1 more source

Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics. [PDF]

, 2018
Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with ...
A El-Tayeb, AD Hanson, AL Hartman, Atsushi Ogiwara, C Fattuoni, C Ruttkies, CL Linster, DY Lee, F Allen, Gert Wohlgemuth, GJ Patti, H Sperber, H Tsugawa, H Tsugawa, H Tsugawa, HD Flosadóttir, Hiroshi Tsugawa, I Yamamoto, J Budczies, JG Jeffryes, John Meissen, K Haug, Kohei Takeuchi, M Sud, Masanori Arita, Matthew Mueller, Megan Showalter, MP Styczynski, MR Showalter, O Fiehn, O Fiehn, O Fiehn, O Khersonsky, Oliver Fiehn, Peter Beal, RR da Silva, S Kim, S Kumari, Sajjan Mehta, SE Stein, SM Rappaport, T Kind, Tobias Kind, WR Wikoff, Yuxuan Zheng, Zijuan Lai   +45 more
core   +1 more source

Deep Learning Approach for Predicting Efficiency in Organic Photovoltaics from 2D Molecular Images of D/A Pairs

Advanced Theory and Simulations, EarlyView.
This study highlights the potential of deep learning, particularly Convolutional Neural Networks (CNNs), for predicting the photovoltaic performance of organic solar cells. By leveraging 2D images representing donor/acceptor molecular pairs, the model accurately estimates key performance indicators proving that this image‐based approach offers a fast ...
Khoukha Khoussa, Patrick Lévêque, Larbi Boubchir   +2 more
wiley   +1 more source

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

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