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1 igh-throughput screening in combination with cheminformatics.
2 allows for efficient cross-vendor structural cheminformatics.
3 neralizable tools for predictive modeling in cheminformatics.
4  the context of computational toxicology and cheminformatics.
5 phenolic profile [HPLC-ESI((-))-qTOF-MS(2)], cheminformatics (ADMET properties, affinity toward plate
6 al toxicity testing results), the integrated cheminformatics algorithm will optimize the extracted bi
7                                              Cheminformatics also pointed out potential off-target ef
8                        Our molecule-centered cheminformatics analyses point to the need to dramatical
9                           These genomics and cheminformatics analyses reveal the large extent to whic
10                                          Our cheminformatics analysis indicates that BaN is an import
11 NMR-based studies coupled with computational cheminformatics and cellular function are leveraged to i
12 synthesis and the possibility of integrating cheminformatics and chemical space analyses with synthet
13      It provides a web interface to a set of cheminformatics and data mining tools that are useful fo
14 current landscape of approved prodrugs using cheminformatics and data science approaches to reveal tr
15 e integration of ligand- and structure-based cheminformatics and experimental approaches for identify
16  these targets in Mtb using a combination of cheminformatics and in vitro screening.
17 -based features that are usually employed in cheminformatics and obtained results of almost similar q
18                           This study applies cheminformatics and quantitative structure-property rela
19                                        Using cheminformatics and quantum chemistry, we analyze the ph
20 erprints while creating new opportunities in cheminformatics and virtual screening.
21   This integrative approach combining LC-MS, cheminformatics, and bioinformatics provides a cost-effe
22 ences to include topics such as climatology, cheminformatics, and machine learning.
23                              Bioinformatics, cheminformatics, and molecular docking/dynamics studies
24  Across the analytical sciences, toxicology, cheminformatics, and regulatory practice, new approaches
25  a holistic overview of machine learning and cheminformatics applications as a novel, powerful means
26 ine learning algorithms is in line with many cheminformatics applications, with SVM and RF being roug
27 of AR and VR contents for bioinformatics and cheminformatics applications.
28                                          Our cheminformatics approach can be applied to identify alte
29 ugh E2 or P4 steroidogenesis, we developed a cheminformatics approach to identify structural features
30 nuum of modern experimental toxicology using cheminformatics approaches and big data sources.
31 te chemical information and biological data, cheminformatics approaches for mapping the extracted che
32                                  Traditional cheminformatics approaches, using learners such as rando
33 n bioinformatics, computational biology, and cheminformatics areas.
34                                              Cheminformatics-based applications to predict transforma
35 bility mass spectrometry in conjunction with cheminformatics-based data integration and visualization
36                                              Cheminformatics-based software tools can predict the mol
37 owledge, provides the first demonstration of cheminformatics-based, complement-directed drug discover
38           The software incorporates advanced cheminformatics capabilities such as chemical structure
39                                              Cheminformatics clustering supported these findings, ide
40 g approach, based on both bioinformatics and cheminformatics components, that enables selecting such
41                                              Cheminformatics data showed that pioglitazone and telmis
42                           Bioinformatics and cheminformatics data support pioglitazone and telmisarta
43  of positives was further evaluated based on cheminformatics/data mining analyses and activity agains
44                                         This cheminformatics-driven retrosynthetic analysis identifie
45            In this study, we have employed a cheminformatics drug discovery approach based on the ext
46 boratories promises to significantly advance cheminformatics, drug discovery, biotechnology and mater
47 he need for specialized computer science and cheminformatics expertise.
48                    Finally, through rigorous cheminformatics featurization strategies and statistical
49 ks and data analysis, bringing complimentary cheminformatics functionality to the workflow environmen
50                     Advances in the field of cheminformatics have been hindered by a lack of freely a
51 a computer-based tool from the U.S. EPA, the Cheminformatics Hazard Comparison Module (HCM), to ident
52 atabase, and (3) hazard values from US EPA's Cheminformatics Hazard Module (CHM).
53                           Using the US-EPA's Cheminformatics Hazard Module, we identified that many o
54 -property relationship analysis, informed by cheminformatics, identified 4s as a potent inhibitor of
55                           Our study provides cheminformatics insights into this privileged, underexpl
56 e overview of the role of representations in cheminformatics, key concepts in deep learning, and argu
57 earchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypo
58 ChEMBL database, as well as the latest RDKit cheminformatics libraries.
59 nal theory, semiempirical quantum mechanics, cheminformatics, linear regression, and machine learning
60 uch as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are d
61 ates theoretical insight, quantum chemistry, cheminformatics, machine learning, industrial expertise,
62                                   Typically, cheminformatics methods operate under the assumption tha
63  have additional chemical data, allowing for cheminformatics mining.
64  drug reactions of drugs with an established cheminformatics modeling method, we generate a data set
65 e and interoperability between complementary cheminformatics packages within the same framework, henc
66 s "virtual space" with real compounds.From a cheminformatics perspective, there is a clear demand for
67                               An open-source cheminformatics pipeline based on MS-DIAL and patRoon wa
68                                 We develop a cheminformatics pipeline that predicts receptor-odorant
69                        A comparison with the cheminformatics plug-in RDKit shows that KNIME-CDK suppo
70 have created Chembench, a publicly available cheminformatics portal for analyzing experimental chemic
71               Triggers were determined using cheminformatics; potentially toxic compounds were select
72                                      Further cheminformatics predictions highlighted systemic toxicit
73 s a proof-of-principle, this high-throughput cheminformatics procedure was applied to the Arabidopsis
74 d graph analysis into the growing scientific/cheminformatics Python stack, increasing the flexibility
75 t from assigning an EC classification from a cheminformatics representation of a reaction.
76 ressing proteins and chemicals with a common cheminformatics representation.
77 data network, as well as across large public cheminformatics resources such as PubChem.
78 ine-entry system (SMILES), which has powered cheminformatics since the late 1980s.
79 ive approach to drug discovery, encompassing cheminformatics, structure-potency and structure-propert
80                     We used a combination of cheminformatics, target-, and phenotypic-based drug disc
81 ould be applied to almost any molecular (bio)cheminformatics tasks.
82  IgE binding epitopes was investigated using cheminformatics techniques.
83 ptions to the raw notebook information using cheminformatics techniques.
84 oactivity data with open source database and cheminformatics technologies.
85                   We review the evolution of cheminformatics, the amalgam of chemistry, computer scie
86 prioritisation and triage of compounds using cheminformatics to balance chemical diversity and drug l
87 mmunological assays, structural biology, and cheminformatics to construct a recombinant M protein-bas
88 ity (VR) in the fields of bioinformatics and cheminformatics to visualize complex biological and chem
89                                              Cheminformatics, together with a "logical" statistical a
90                             A workflow-based cheminformatics tool provides the advantage of ease-of-u
91                                      Such a "cheminformatics toolbox" would enable chemists and biolo
92                              Thus, utilizing cheminformatics tools such as metabolomics and computer-
93 de in the lack of adequate computational and cheminformatics tools that are able to support the analy
94 nually, there is a growing need for advanced cheminformatics tools to prioritize substances of concer
95 h high-resolution mass spectrometry and open cheminformatics tools.
96 nity and can be easily integrated with other cheminformatics tools.
97 ing mass-spectrometry-based methods and open cheminformatics tools.
98  identified through the integration of three cheminformatics tools: BinVestigate, which queries the B
99 iCLE), a high-performance computing-friendly cheminformatics workflow for generating libraries of che