Validation strategies for target prediction methods.

Area of Science:

• Computational chemistry and cheminformatics
• Drug discovery and development
• Bioinformatics and systems biology

Background:

• Computational methods, including molecular similarity, network-based approaches, machine learning, and docking, are vital for identifying the mode of action of bioactive small molecules.
• Accurate assessment of these target prediction methods is essential for their effective application in drug discovery.
• Current validation practices often rely on retrospective statistical validation due to the infeasibility of large-scale prospective experiments.

Purpose of the Study:

• To review and critically analyze the various validation strategies used for computational target prediction methods.
• To highlight the strengths, limitations, and constraints of commonly employed validation schemes and performance metrics.
• To propose considerations for more detailed and realistic performance estimations, addressing data biases in existing bioactivity and structural datasets.

Main Methods:

• Literature review and critical analysis of validation strategies for computational target prediction.
• Discussion of statistical validation techniques, including retrospective approaches.
• Examination of performance metrics and their limitations, particularly concerning data biases.

Main Results:

• Multiple statistical validation techniques exist, varying significantly in their rigor and applicability.
• Generalized performance metrics can be misleading due to inherent biases in small-molecule and target family data.
• The review identifies limitations in current validation practices and suggests areas for improvement.

Conclusions:

• Thorough validation is critical for understanding the scope and limitations of computational target prediction tools.
• Moving beyond generalized performance metrics to consider additional aspects is necessary for more realistic assessments.
• The review provides insights into the validation strategies of established target prediction methods.