Approximate Models and Noise

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Approximate Models and Noise
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State of the Art

• Sources of uncertainty
• Uncertainty in inputs
• Uncertainty in external factors
• Uncertainty model output
• Uncertainty in constraints
• Sources of noise in models
• Experimental noise
• Lack of coverage of models
• Inaccurate/incomplete validation
• Choice/availability of descriptors
• How do we deal with this
• Probabilistic modelling
• Robustness techniques sensitivity to noise
• Normal distributions

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Problems

• Dont fully understand form of probability
  distributions
• Prior distributions
• No data!
• Descriptors typically have low information content

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Promising Approaches

• Multiple models based on different approaches
  consensus
• But need multiple sets of training data
• Global vs local models
• Non-dimensional transforms (Buckingham Theorem)
  to reduce noise in input data
• E.g. pKi vs Ki
• But, are there other approaches?
• Distribution fitting to data (when/if available)
• Better models, accuracy and transferability
• E.g. quantum mechanical descriptors
• Capture underlying physical model
• Estimate of inaccuracy of current models
• More data
• Directly comparable data where from?
• Use computationally expensive calculations as
  input to empirical methods but still
  limitations to accuracy