AI and Machine Learning for Proteomics-Driven Drug Discovery: Methods, Tools, and Best Practices.

Area of Science:

• Proteomics and Bioinformatics
• Pharmacological Research
• Computational Biology

Background:

• Proteomics provides crucial quantitative data on protein abundance, modifications, interactions, and localization.
• Proteomic datasets are complex, high-dimensional, and often suffer from missing values, batch effects, and small sample sizes.
• AI and ML offer powerful tools to extract meaningful insights from complex proteomic data.

Purpose of the Study:

• To critically compare various AI and ML approaches for proteomics-driven drug discovery.
• To summarize key software ecosystems relevant to proteomic data analysis and drug development.
• To highlight challenges and emerging trends in applying AI/ML to proteomics.

Main Methods:

• Review and critical comparison of supervised learning, ensemble methods, dimensionality reduction, clustering, deep learning, graph learning, survival modeling, causal inference, and calibration.
• Summary of software for mass spectrometry processing, targeted assays, spectrum prediction, phosphoproteomics, structure modeling, and reproducible workflows.
• Emphasis on model selection, benchmarking, missing data imputation, batch correction, interpretability, uncertainty quantification, experimental validation, and translational readiness.

Main Results:

• AI/ML methods can transform complex proteomic data into actionable outputs for drug discovery.
• A comprehensive overview of current AI/ML techniques and software tools applicable to proteomic analysis is provided.
• Key considerations for successful implementation, including data quality, model choice, and validation, are discussed.

Conclusions:

• AI and ML are essential for unlocking the full potential of proteomics in drug discovery and development.
• Addressing data challenges like missingness and batch effects is critical for reliable AI/ML model performance.
• Emerging AI/ML techniques like contrastive learning and federated analytics promise further advancements in the field.