Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors.
about
Identification of novel functional inhibitors of acid sphingomyelinaseIdentification of drugs inducing phospholipidosis by novel in vitro dataDeriving therapies for children with primary CNS tumors using pharmacokinetic modeling and simulation of cerebral microdialysis dataDeveloping Enhanced Blood-Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR ModelingTowards Better BBB Passage Prediction Using an Extensive and Curated Data Set.DemQSAR: predicting human volume of distribution and clearance of drugs.Blood-brain barrier transport studies, aggregation, and molecular dynamics simulation of multiwalled carbon nanotube functionalized with fluorescein isothiocyanate.3D pharmacophoric similarity improves multi adverse drug event identification in pharmacovigilance.Use of in-silico assays to characterize the ADMET profile and identify potential therapeutic targets of fusarochromanone, a novel anti-cancer agentEvaluation of the behavioral and pharmacokinetic profile of SYA013, a homopiperazine analog of haloperidol in ratsCNS Anticancer Drug Discovery and Development Conference White Paper.A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction.Physiologically based pharmacokinetic modelling of drug penetration across the blood-brain barrier--towards a mechanistic IVIVE-based approachDelivery of molecularly targeted therapy to malignant glioma, a disease of the whole brain.Improving the prediction of drug disposition in the brain.Can we predict blood brain barrier permeability of ligands using computational approaches?Permeability of the Blood-Brain Barrier: Molecular Mechanism of Transport of Drugs and Physiologically Important Compounds.In Silico Prediction for Intestinal Absorption and Brain Penetration of Chemical Pesticides in Humans.Synthesis and Determination of Physicochemical Properties of New 3-(4-Arylpiperazin-1-yl)-2-hydroxypropyl 4-Alkoxyethoxybenzoates.Qualitative prediction of blood-brain barrier permeability on a large and refined dataset.A Generally Applicable Computer Algorithm Based on the Group Additivity Method for the Calculation of Seven Molecular Descriptors: Heat of Combustion, LogPO/W, LogS, Refractivity, Polarizability, Toxicity and LogBB of Organic Compounds; Scope and LiQualitative and quantitative structure-activity relationship modelling for predicting blood-brain barrier permeability of structurally diverse chemicals.Development of a Region-Specific Physiologically Based Pharmacokinetic Brain Model to Assess Hippocampus and Frontal Cortex Pharmacokinetics.Subnanomolar indazole-5-carboxamide inhibitors of monoamine oxidase B (MAO-B) continued: indications of iron binding, experimental evidence for optimised solubility and brain penetration.Inhibition of flavonoids on acetylcholine esterase: binding and structure-activity relationship.Prediction of Lipophilicity and Pharmacokinetics of Chloroacetamides by Chemometric Approach.The Mu.Ta.Lig. Chemotheca: A Community-Populated Molecular Database for Multi-Target Ligands Identification and Compound-Repurposing.Insight into the Functional and Structural Properties of 3-Arylcoumarin as an Interesting Scaffold in Monoamine Oxidase B Inhibition
P2860
Q21003953-674C5F46-FA09-4B7B-B5AE-552EC358E3B5Q30560958-9D293F4A-5AF0-4B74-800B-3A778DF988BBQ30700231-802FC5DD-D389-4975-883D-69CBA4E890A3Q30929535-145F71F5-536F-49B4-8600-D53FAC0C0C84Q31119557-41DC3F55-2462-494D-A687-397E77F580FBQ34078930-B7318E63-E42B-4D83-A6BB-8B7041DBD5C1Q34467660-0E4C6734-D5D0-4C0E-BFEC-D02AF53843B5Q35153278-DC0D1AB0-5166-4F12-B92D-BA6D40E35C51Q35729725-F7ED88A6-A084-4B87-A4A0-487B1A518B8EQ36059043-D1506BA4-3832-4347-84B4-A153338CE9B9Q36089590-AA939CE6-C08F-4455-B474-69F0AE246D69Q36172940-D2B9D418-22C0-4D8F-8C1C-37119F5537BCQ37207750-9C2B61F7-B752-4D3C-A550-6AFAFB7F12D6Q37308322-CCA96A53-2259-42AC-BDCE-7E1EDCE6D082Q38072077-1630BF68-12F9-411C-80F1-1168AE49AC5DQ38112372-B94DF9BC-B665-41AB-AFF4-920E2BFDE782Q38353145-97CE8F10-D5A6-45CE-9117-CF0BC29B1BD2Q38700355-4E913803-9608-4E71-8F1C-DD8EFAB2858CQ39110578-7AFE7837-D6D8-4E39-A946-D52C6B324ADDQ39896264-8B7F83E8-194E-4A69-BEC0-F212D3588F26Q40441933-896FD935-7B6F-444E-9773-EE53BCF68684Q41514243-5ED85445-6E69-48C8-92E9-46A66B639CDDQ47860080-4ED2A037-F63C-4AFE-B79E-15A8657D1BB1Q47964614-3A239458-F3FC-4D6A-93D0-8A83ECB65DB2Q53468640-4ED5B7AE-5B5F-4C7F-8E83-FD235344B421Q55098390-5C1684E5-BDD2-48F8-852B-1D07000DA9CDQ55257398-2B5B1C83-2C82-4D7E-94A5-D83D3C65EED8Q57412727-5450BD2E-58F0-45F8-9BEA-C1C0C5286DBF
P2860
Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors.
description
2010 nî lūn-bûn
@nan
2010 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
Prediction of passive blood-br ...... d physicochemical descriptors.
@ast
Prediction of passive blood-br ...... d physicochemical descriptors.
@en
type
label
Prediction of passive blood-br ...... d physicochemical descriptors.
@ast
Prediction of passive blood-br ...... d physicochemical descriptors.
@en
prefLabel
Prediction of passive blood-br ...... d physicochemical descriptors.
@ast
Prediction of passive blood-br ...... d physicochemical descriptors.
@en
P2093
P2860
P1476
Prediction of passive blood-br ...... d physicochemical descriptors.
@en
P2093
Mayukh Chakrabarti
Santiago Vilar
Stefano Costanzi
P2860
P304
P356
10.1016/J.JMGM.2010.03.010
P577
2010-04-03T00:00:00Z