Define AI readiness and its underlying principles through our new research.

Summary: Over 100 talks from posit::conf(2025) on YouTube, featuring R, Python, Quarto, Posit tools, and experts like Hadley Wickham, Wes McKinney, and Rich Iannone.

Abstract. The ProteomeXchange consortium of proteomics resources (http://www.proteomexchange.org) was established to standardize open data practices in the

Deep phenotyping of an ancestrally diverse group of 13,000 individuals in the Human Phenotype Project highlights diversity and variations in lifestyle factors, clinical features and molecular…

A bioinformatics best-practice analysis pipeline for Quantitative Mass Spectrometry (MS)

Targeted covalent inhibitors possess advantages over reversible binding drugs, that include higher potency, enhanced selectivity and prolonged pharmacodynamic duration. The standard paradigm for coval...

imageimagehu.MAP3.0 integrates mass spectrometry experiments to identify human protein complexes. Using this resource, this study characterizes covariation of complexes, identifies mutually exclusive ...

Computational hit finding, particularly virtual screening, has been a mainstay of drug discovery campaigns for decades, providing a cost-efficient complement to wet experiments. Innovation in this spa...

Chemical Biology in the PUB

Machine learning (ML) models hold the promise of transforming atomic simulations by delivering quantum chemical accuracy at a fraction of the computational cost. Realization of this potential would en...

Protein–protein interactions specific to 11 individual tissues are presented in a comprehensive resource.

Scientific discovery relies on innovative software as much as experimental methods, especially in proteomics, where computational tools are essential for mass spectrometer setup, data analysis, and interpretation. Since the introduction of SEQUEST, proteomics software has grown into a complex ecosystem of algorithms, predictive models, and workflows, but the field faces challenges, including the increasing complexity of mass spectrometry data, limited reproducibility due to proprietary software, and difficulties integrating with other omics disciplines. Closed-source, platform-specific tools exacerbate these issues by restricting innovation, creating inefficiencies, and imposing hidden costs on the community. Open-source software (OSS), aligned with the FAIR Principles (Findable, Accessible, Interoperable, Reusable), offers a solution by promoting transparency, reproducibility, and community-driven development, which fosters collaboration and continuous improvement. In this manuscript, we explore the role of OSS in computational proteomics, its alignment with FAIR principles, and its potential to address challenges related to licensing, distribution, and standardization. Drawing on lessons from other omics fields, we present a vision for a future where OSS and FAIR principles underpin a transparent, accessible, and innovative proteomics community.

Colab Notebooks covering deep learning tools for biomolecular structure prediction and design - Graylab/DL4Proteins-notebooks

Ongoing advancements in instrumentation has established mass spectrometry (MS) as an essential tool in proteomics research and drug discovery. The newly released Asymmetric Track Lossless (Astral) ana...

Computational tools for structure-based drug design (SBDD) are widely used in drug discovery and can provide valuable insights to advance projects in an efficient and cost-effective manner. However, d...