Results 41 to 50 of about 12,214 (208)

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-Python

, 2019
This repository is mainly to store programs written and used in my thesis project for my Master of Chemistry degree.
Andrew Cornell (5892917)
core   +1 more source

Cheminformatics and the Semantic Web: adding value with linked data and enhanced provenance

, 2013
Cheminformatics is evolving from being a field of study associated primarily with drug discovery into a discipline that embraces the distribution, management, access, and sharing of chemical data.
Jeremy G. Frey, Frey, Jeremy G., Colin L. Bird, Bird, Colin   +3 more
core   +1 more source

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

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

Adsorption of Forever Chemical Pollutants: The Physical Chemistry of PFAS Near Surfaces

Advanced Science, EarlyView.
Current adsorption‐based remediation techniques for removing per‐ and polyfluoroalkyl substances (PFAS) from water are limited by knowledge gaps on PFAS behavior near solid surfaces. This review provides a state of the art on theoretical and experimental aspects of PFAS adsorption.
Nada Ben Amor, Daniela Bauer, Benjamin Braconnier, Benoit Coasne   +3 more
wiley   +1 more source

Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules [PDF]

, 2014
We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values.
van Mourik, Tanja, James L. McDonagh, Tanja van Mourik (473039), De Ferrari, Luna; id_orcid, McDonagh, James L, Neetika Nath, John B. O. Mitchell, Neetika Nath (569834), De Ferrari, Luna, Luna De Ferrari (1312536), John B. O. Mitchell (22057), McDonagh, James, James L. McDonagh (1312533), Luna De Ferrari, Tanja van Mourik, Mitchell, John B O, Nath, Neetika, Mitchell, John B. O.   +17 more
core   +1 more source

Lessons From Drug Discovery for Cryoprotective Agent Design: An AI‐Oriented Perspective

Advanced Science, EarlyView.
Cryoprotectant design is reframed through the lens of drug discovery as a multiparameter optimization problem. This perspective highlights how AI and systematic design strategies could enable safer, more effective cryoprotectants, while identifying key limitations that currently constrain predictive progress in cryobiology. ABSTRACT Cryopreservation is
Dominika Wilczok, Jesús Valdés‐Hernández, Varinia Bernales, Alán Aspuru‐Guzik, Alex Zhavoronkov   +4 more
wiley   +1 more source

Advancing the Design of High‐Efficiency Printable Hole‐Conductor‐Free Mesoscopic Perovskite Solar Cells Through Machine Learning

Advanced Science, EarlyView.
Based on the largest printable mesoscopic perovskite solar cells database we established, stacking model achieved precise PCE prediction (R2 = 0.73, MAE = 2.18%). Multiple experiments verified the accuracy of the model, which guided the fabrication of high‐PCE devices with an efficiency of 19.36%.
Hao Meng, Jingzi Zhang, Xu Zhu, Yuelin Wang, Antai Yang, Kailong Hu, Chengquan Zhong, Jiakai Liu, Menghan Dun, Xi Lin   +9 more
wiley   +1 more source