Hi
good day
Can you suggest a solution to build COMFA and COMSIA models with better Q2?
Thank you in advance.
Sargol Mazraedoost
Molecular Alignment: Refine the alignment of your molecules: Accurate alignment of the molecules can significantly impact the model's performance. Ensure that you use the best available alignment method. Explore different alignment methods: Test various alignment techniques, such as molecular superimposition, substructure-based alignment, or shape-based alignment, to find the most suitable one for your dataset. Descriptors: Descriptor selection: Review the descriptors used in your model. Consider using different sets of descriptors and assess their impact on the model's Q2. Cross-Validation: Cross-validation technique: Experiment with different cross-validation methods (e.g., leave-one-out, k-fold) to ensure the robustness of your model. Variable Selection: Feature selection: Employ techniques to select the most relevant descriptors and remove irrelevant ones, which can improve model performance. Data Preprocessing: Data scaling: Normalize or standardize your data to ensure that all descriptors are on a similar scale. Hyperparameter Tuning: Model parameters: Fine-tune the parameters of your CoMFA or CoMSIA model. This may include grid searches or optimization techniques to find the best parameter combinations. Dataset Size and Diversity: Dataset expansion: Consider adding more data points to your dataset, increasing its diversity and coverage of the chemical space. Outliers Removal: Identify and remove outliers in your dataset, which can distort model performance. Validation Set: Use an independent validation set: Ensure that your model is validated on a separate dataset not used in training to assess its predictive power.
Here are some strategies to consider:
0 votes
0 thanks
- Badges
- Science topic
- Similar topics
- Physical Sciences
- Atomic, Molecular and Optical Physics
- Atoms
Similar questions and discussions