Related Experiment Video
Updated: Jun 2, 2026
12:31
In Vivo Modeling of the Morbid Human Genome using Danio rerio
Published on: August 24, 2013
Trade-off between accuracy and interpretability for predictive in silico modeling.
Ulf Johansson1, Cecilia Sönströd, Ulf Norinder
1School of Business & Informatics, University of Borås, SE-50190, Sweden. ulf.johansson@hb.se
Future Medicinal Chemistry
|May 11, 2011
Summary
This study compares accurate but complex models with interpretable yet less accurate ones. Results show that interpretable models offer a good balance, with only a small drop in predictive performance for biopharmaceutical classification tasks.
Area of Science:
- Computational chemistry
- Bioinformatics
- Machine learning
Background:
- Investigating the trade-off between model accuracy and interpretability in predictive in silico modeling.
- Accurate models are often complex and opaque, while interpretable models may lack predictive power.
Purpose of the Study:
- To compare state-of-the-art methods for generating accurate predictive models with those for generating transparent models.
- To evaluate the performance penalty associated with choosing interpretable models over opaque ones.
Main Methods:
- Comparison of state-of-the-art accurate modeling techniques against state-of-the-art transparent modeling techniques.
- Evaluation across 16 distinct biopharmaceutical classification tasks.
Main Results:
- Opaque methods generally achieved higher accuracy than transparent methods.
- The performance penalty for selecting interpretable models was often limited.
Conclusions:
- Interpretable models provide a viable alternative when a balance between accuracy and understanding is desired.
- Limited predictive performance loss supports the use of interpretable models in biopharmaceutical classification.
Related Concept Videos
02:07Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
02:07Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
01:26Pharmacokinetic Models: Comparison and Selection Criterion
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
01:15Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions
PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
01:27Pharmacodynamic Models: Overview
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
01:03Prediction Intervals
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...