research Pharmacophore Models for Toxicology Prediction
Pharmacophore models can predict liver toxicity and central nervous system toxicity, but they have limitations and specific requirements.
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research Pharmacophore Modeling and Three Dimensional Database Searching for Drug Design Using Catalyst: Recent Advances
The document concludes that Catalyst software is effective for drug design, identifying potent compounds for various medical conditions.
research Development and Qualification of a Physiologically Based Pharmacokinetic Model of Finasteride and Minoxidil Following Scalp Application
The model can help predict how finasteride and minoxidil work when applied to the scalp.
research A Simple Decision Tree Suited for Identification of Early Oral Drug Candidates With Likely Pharmacokinetic Nonlinearity by Intestinal CYP3A Saturation
The decision tree can predict drug absorption issues with good accuracy but needs more validation and adjustments for other factors.
research A machine learning and network framework to discover new indications for small molecules
The model successfully predicted new uses for existing drugs, like using certain hormonal and heart medications for respiratory and Parkinson's diseases, and a cancer drug for diabetes.
research Harmonic Vibrational Frequency Simulation of Pharmaceutical Molecules via a Novel Multi-Molecular Fragment Interception Method
The new method provides more accurate vibrational frequencies for drug molecules than traditional models.
research Pharmacophore and Atom Based 3D QSAR Studies on the Novel 5-Alpha-Reductase Inhibitors
The developed model can predict effective 5-alpha-reductase enzyme inhibitors.
research Predictive Skin Permeation of Ethinylestradiol,α-Asarone,Finasteride and Risperidone
The Potts-Guy model best predicts skin permeability for the tested drugs.
research Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways
Mathematical modeling helps understand and predict the MAPK cell signaling pathway.
research Optimization of finasteride nano-emulsion preparation by using chemometrics approaches (and/or Box-Behnken design and regresion model)
research Application of big data analytics in minoxidil drug development: a data-driven model for predicting efficacy and side effect risks
The model predicts minoxidil's effectiveness and side effects better than traditional methods.
research A machine learning and network framework to discover new indications for small molecules
A machine learning model called CATNIP can predict new uses for existing drugs, like using antidepressants for Parkinson's disease and a thyroid cancer drug for diabetes.
research Modeling cell–cell communication for immune systems across space and time
The document concludes that computational models are useful for understanding immune responses and could improve cancer immunotherapy.
research Regenerative medicine meets mathematical modelling: developing symbiotic relationships
Mathematical modeling can improve regenerative medicine by predicting biological processes and optimizing therapy development.
research Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks
The method effectively predicts new drug uses, including potential COVID-19 treatments.
research Development of a Two-Dimensional Model for Predicting Transdermal Permeation with the Follicular Pathway: Demonstration with a Caffeine Study
Researchers developed a model that shows hair follicles increase skin absorption of caffeine by 20%.
research Homology Modeling of 5-alpha-Reductase 2 Using Available Experimental Data
Accurate protein modeling can help develop new treatments for prostate cancer and other diseases.
research Abstract ED05-04: Presurgical models to effectively screen preventive agents
Presurgical models can effectively and affordably screen cancer prevention agents.
research QSAR Analysis of a Series of Hydantoin‐based Androgen Receptor Modulators and Corresponding Binding Affinities
Researchers developed reliable models to predict how well certain compounds bind to androgen receptors, emphasizing the importance of atomic electronegativity.
research 5α-Reductase Inhibitors, Chemical and Clinical Models
Researchers developed a model to predict how well certain compounds can block an enzyme related to hair loss and prostate issues, suggesting a 50 mg dose of finasteride might be effective based on lab and body data.
research A weighted non-negative matrix factorization approach to predict potential associations between drug and disease
WNMFDDA effectively predicts drug-disease associations.
research A Composite Model for the Transport of Hydrophilic and Lipophilic Compounds Across the Skin: Steady-State Behavior
The model better predicts how water-loving and fat-loving substances move through the skin by including tiny pores and hair follicle paths.
research DrugNet: Network-based drug–disease prioritization by integrating heterogeneous data
DrugNet effectively identifies new uses for existing drugs and may save resources in drug development.
research Social Pharmacology: Integrating Pharmaceutical and Social Science Research on Drug Effects
The conclusion is that combining social and cultural factors with pharmaceutical research could improve our understanding of how drugs work.
research Mathematical Modeling of Alopecia Areata: Unraveling Hair Cycle Dynamics, Disease Progression, and Treatment Strategies
The model helps understand alopecia areata and suggests better treatment strategies.
research Bayesian measurement-error-driven hidden Markov regression model for calibrating the effect of covariates on multistate outcomes: Application to androgenetic alopecia
Model improves accuracy in predicting hair loss effects.
research Hair keratin molecular dynamics models
Molecular dynamics simulations help understand keratin's properties and predict hair's response to treatments.
research Using risk analysis to guide changes to compounding formulae
Risk analysis is important for safely adjusting pharmaceutical formulas while maintaining their quality and effectiveness.
research KRDQN: An Interpretable Prediction Framework for Adverse Drug Reactions via Knowledge–Graph Reinforced Deep Q-Learning
KRDQN effectively predicts adverse drug reactions with high accuracy and clear explanations.
research Modeling share dynamics by extracting competition structure
The model can predict website market shares by identifying competition among them.