about
Systems Pharmacology in Small Molecular Drug DiscoveryComputational methods for prediction of in vitro effects of new chemical structuresCurrent status and future prospects for enabling chemistry technology in the drug discovery processUsing Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases.Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties.In vitro assessment of a computer-designed potential anticancer agent in cervical cancer cells.Quantitative structure-activity relationship (QSAR) directed the discovery of 3-(pyridin-2-yl)benzenesulfonamide derivatives as novel herbicidal agents.Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitorsBiophysical Approaches Facilitate Computational Drug Discovery for ATP-Binding Cassette Proteins.Weak-binding molecules are not drugs?-toward a systematic strategy for finding effective weak-binding drugs.Functional amyloids: interrelationship with other amyloids and therapeutic assessment to treat neurodegenerative diseases.Tailored Approaches in Drug Development and Diagnostics: From Molecular Design to Biological Model Systems.Exploring β-Tubulin Inhibitors from Plant Origin using Computational Approach.Cannabinoid Type 1 Receptor (CB1) Ligands with Therapeutic Potential for Withdrawal Syndrome in Chemical Dependents of Cannabis sativa.Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.MetStabOn-Online Platform for Metabolic Stability Predictions.ProTox-II: a webserver for the prediction of toxicity of chemicals.ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database.
P2860
Q26768459-8071CA71-7D1D-4A98-9264-AA1A5DEE6B85Q27902263-68F26336-28D3-477E-941E-2495D366899DQ28066266-A66A5D1C-D0D1-40D8-A758-16A52CECF176Q31138564-BA41FD51-F14D-4DEA-A810-A7B48688D9E6Q36333632-05B46DA9-3877-4894-8266-D3B92744BE8BQ37390763-148E79E6-B425-4EE6-9FD8-01F9E04C40F4Q38649717-11242A5E-3394-4882-B870-AF08FFC4B460Q38732067-D0451656-1F79-4F95-B22B-ED6E6A8BE79AQ38837398-87DAC694-A365-47F9-A5D3-1D8E3E501E6EQ38897226-00B98CB0-7D94-4F5B-9A5A-D62632824955Q47404503-E32D012C-065F-4DA5-83E2-A320452ECF8BQ47690085-12056481-81FC-47DD-BA9C-69FE4E050957Q48032873-D72EE05F-F57C-4EC9-B8BB-E353C0EA0AC5Q48267092-888C2876-C9C3-4CD2-AC46-9F5E2A5905D3Q50420935-AAFDB308-1102-497D-BC79-934D6D75C4A9Q52337060-898899C3-AD89-45FD-8148-DDB5CE543BA0Q52803587-DF0EF00A-0E55-4EFF-AD23-37789EAA22F1Q55646607-D07EDD80-9D32-4626-B9A5-7B3DFF5411C9
P2860
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
In silico ADME/T modelling for rational drug design.
@en
type
label
In silico ADME/T modelling for rational drug design.
@en
prefLabel
In silico ADME/T modelling for rational drug design.
@en
P2093
P2860
P50
P1476
In silico ADME/T modelling for rational drug design
@en
P2093
Hualiang Jiang
Jianlong Peng
Kaixian Chen
Nannan Zhou
Xiaomin Luo
Yulan Wang
P2860
P304
P356
10.1017/S0033583515000190
P577
2015-09-02T00:00:00Z