难受
发表于 2025-3-21 17:31:18
书目名称In Silico Methods for Predicting Drug Toxicity影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0462972<br><br> <br><br>书目名称In Silico Methods for Predicting Drug Toxicity影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0462972<br><br> <br><br>书目名称In Silico Methods for Predicting Drug Toxicity网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0462972<br><br> <br><br>书目名称In Silico Methods for Predicting Drug Toxicity网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0462972<br><br> <br><br>书目名称In Silico Methods for Predicting Drug Toxicity被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0462972<br><br> <br><br>书目名称In Silico Methods for Predicting Drug Toxicity被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0462972<br><br> <br><br>书目名称In Silico Methods for Predicting Drug Toxicity年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0462972<br><br> <br><br>书目名称In Silico Methods for Predicting Drug Toxicity年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0462972<br><br> <br><br>书目名称In Silico Methods for Predicting Drug Toxicity读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0462972<br><br> <br><br>书目名称In Silico Methods for Predicting Drug Toxicity读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0462972<br><br> <br><br>
Neuropeptides
发表于 2025-3-21 23:00:07
In Silico 3D Modeling of Binding Activitiesr homology modeling of receptors have been reliably used in pharmacological research and development for decades. Molecular docking methodologies are helpful for revealing facets of activation and inactivation, thus improving mechanistic understanding and predicting molecular ligand binding activity
赔偿
发表于 2025-3-22 03:29:39
Modeling Pharmacokineticsescriptions of xenobiotics’ absorption, distribution, metabolism, and excretion processes. They model the body as a set of homogeneous compartments representing organs, and their parameters refer to anatomical, physiological, biochemical, and physicochemical entities. They offer a quantitative mecha
abnegate
发表于 2025-3-22 07:49:11
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POWER
发表于 2025-3-22 12:28:00
In Silico Prediction of Chemically Induced Mutagenicity: How to Use QSAR Models and Interpret Their AR models that can predict Ames genotoxicity are freely available for download from the Internet and they can provide relevant information for the toxicological profiling of chemicals. Indeed, they can be straightforwardly used for predicting the presence or absence of genotoxic hazards associated w
Ordeal
发表于 2025-3-22 13:27:45
In Silico Methods for Carcinogenicity Assessmentative predictive models, ranging from short-term biological assays (e.g. mutagenicity tests) to theoretical models, have been attempted in this field. Theoretical approaches such as (Q)SAR are highly desirable for identifying carcinogens, since they actively promote the replacement, reduction, and r
Intruder
发表于 2025-3-22 19:32:14
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姑姑在炫耀
发表于 2025-3-22 22:09:29
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旧式步枪
发表于 2025-3-23 03:58:53
In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Leve is the determination of the no observed adverse effect level (NOAEL) and the lowest observed adverse effect level (LOAEL). NOAEL is important since it serves to calculate the maximum recommended starting dose (MRSD) which is the safe starting dose for clinical studies in human beings. Since in vivo
Blemish
发表于 2025-3-23 08:37:31
In Silico Models for Acute Systemic Toxicityvailability of structure-based computational models that are available and potentially useful in the assessment of acute systemic toxicity. The most recently published literature models for acute systemic toxicity are also discussed, and perspectives for future developments in this field are offered