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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Quantitative Aspects of Drug-Receptor Interaction

Quantitative Aspects of Drug-Receptor Interaction

The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...

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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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Uncertainty quantification: Can we trust artificial intelligence in drug discovery?

Jie Yu^1,2, Dingyan Wang^1,2, Mingyue Zheng^1,2

¹Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.

|August 23, 2022

Summary

Quantifying uncertainty in artificial intelligence models is crucial for reliable drug discovery. This approach enhances trust and guides researchers in molecular reasoning and experimental design for safer drug development.

Keywords:

Applied computing Artificial intelligence Drugs

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Area of Science:

Computational chemistry
Artificial intelligence in drug discovery
Machine learning for cheminformatics

Background:

In silico models accelerate drug discovery but have limited applicability domains.
Predictions outside the training data's chemical space are unreliable and potentially hazardous.
Human trust in AI predictions is a fundamental challenge in AI-driven drug design.

Purpose of the Study:

To summarize state-of-the-art uncertainty quantification (UQ) methods.
To highlight the application of UQ in drug design and discovery.
To outline representative UQ scenarios in pharmaceutical research.

Main Methods:

Review of current uncertainty quantification techniques relevant to machine learning models.
Analysis of how UQ enhances the reliability of in silico predictions.
Identification and description of key application areas for UQ in drug discovery.

Main Results:

UQ provides a quantitative measure of prediction confidence.
Reliability of AI model predictions can be assessed, aiding decision-making.
UQ supports researchers in molecular reasoning and experimental planning.

Conclusions:

Uncertainty quantification is essential for building trust in AI for drug discovery.
UQ enables more autonomous and reliable drug design processes.
Implementing UQ can significantly improve the efficiency and safety of pharmaceutical R&D.