Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Alexios Koutsoukas"'
Publikováno v:
Journal of Cheminformatics, Vol 9, Iss 1, Pp 1-13 (2017)
Abstract Background In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Recently reported success of DL techniques in crowd-sourced QSAR and predictive toxicology co
Externí odkaz:
https://doaj.org/article/b3ecb9e27e234057ac0f5202175666f9
Autor:
Fazlin Mohd Fauzi, Alexios Koutsoukas, Robert Lowe, Kalpana Joshi, Tai-Ping Fan, Robert C Glen, Andreas Bender
Publikováno v:
Journal of Ayurveda and Integrative Medicine, Vol 4, Iss 2, Pp 117-119 (2013)
In this article, we discuss our recent work in elucidating the mode-of-action of compounds used in traditional medicine including Ayurvedic medicine. Using computational (′in silico′) approach, we predict potential targets for Ayurvedic anti-canc
Externí odkaz:
https://doaj.org/article/8eca2cfa0def47b7a075f2c538b1681d
Publikováno v:
The Journal of chemical physics. 154(22)
The message passing neural network (MPNN) framework is a promising tool for modeling atomic properties but is, until recently, incompatible with directional properties, such as Cartesian tensors. We propose a modified Cartesian MPNN (CMPNN) suitable
Autor:
David Sherrill, Daniel Cheney, Steven Spronk, Alexios Koutsoukas, Derek Metcalf, Zachary Glick
Intermolecular interactions are critical to many chemical phenomena, but their accurate computation using ab initio methods is often limited by computational cost. The recent emergence of machine learning (ML) potentials may be a promising alternativ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8ff3a2e58b37e1b1872b5a9038add984
https://doi.org/10.26434/chemrxiv.12246020
https://doi.org/10.26434/chemrxiv.12246020
Autor:
Jeffrey B. Schriber, Steven A. Spronk, C. David Sherrill, Daniel L. Cheney, Daniel R. Nascimento, Alexios Koutsoukas
Publikováno v:
The Journal of Chemical Physics. 154:184110
Computation of intermolecular interactions is a challenge in drug discovery because accurate ab initio techniques are too computationally expensive to be routinely applied to drug–protein models. Classical force fields are more computationally feas
Publikováno v:
Journal of chemical information and modeling. 59(1)
Matched molecular pair analysis (MMPA) has emerged as a powerful approach to mine and extract tacit knowledge from measured databases of small molecules. Extracted knowledge from past experimentation can assist future lead optimization as an idea gen
Autor:
Alexios Koutsoukas, C. David Sherrill, Steven A. Spronk, Daniel L. Cheney, Zachary L. Glick, Derek P. Metcalf
Publikováno v:
The Journal of Chemical Physics. 153:044112
Intermolecular interactions are critical to many chemical phenomena, but their accurate computation using ab initio methods is often limited by computational cost. The recent emergence of machine learning (ML) potentials may be a promising alternativ
Autor:
C. David Sherrill, Stephen R. Johnson, Daniel L. Cheney, Brian L. Claus, Derek P. Metcalf, Steven A. Spronk, Deborah A. Loughney, Alexios Koutsoukas
Publikováno v:
The Journal of Chemical Physics. 152:074103
Accurate prediction of intermolecular interaction energies is a fundamental challenge in electronic structure theory due to their subtle character and small magnitudes relative to total molecular energies. Symmetry adapted perturbation theory (SAPT)
Autor:
Adriaan P. IJzerman, Shardul Paricharak, David Marcus, David R. Spring, Andreas Bender, Robert C. Glen, Alexios Koutsoukas, Warren R. J. D. Galloway
Publikováno v:
Journal of Chemical Information and Modeling. 54:230-242
Chemical diversity is a widely applied approach to select structurally diverse subsets of molecules, often with the objective of maximizing the number of hits in biological screening. While many methods exist in the area, few systematic comparisons u
Autor:
Andreas Bender, Mateusz Maciejewski, Georgios Drakakis, Robert C. Glen, Alexios Koutsoukas, Fazlin Mohd Fauzi, Ha P. Nguyen
Publikováno v:
Chemical Biology & Drug Design. 82:252-266
Diversity selection is a frequently applied strategy for assembling high-throughput screening libraries, making the assumption that a diverse compound set increases chances of finding bioactive molecules. Based on previous work on experimental 'affin