How a novel machine learning system is bridging the gap between advanced models and expert insights to accelerate protein research Proteins are essential molecules with many critical roles in the body. Determining a protein’s 3D structure helps scientists understand its functions within cells. This is crucial for advances in biology, medicine, and drug design.

Europe PMC and Open Targets develop Lit-OTAR framework unearthing over 48 million unique associations that can be leveraged for drug discovery Identifying drug targets is a critical and intricate part of drug discovery. This requires scientists to look at many sources of evidence. They use them to find links between drugs, targets, and diseases.

Preprinting has soared in popularity in the life sciences and is increasingly recognised as an excellent method to share research outputs quickly and freely. While preprints have gained popularity, an important consideration is ensuring their scientific quality. This prompted the development of platforms where researchers can comment on, evaluate, and review preprints.

The practice of preprinting in the life sciences has grown rapidly. In addition to accelerating scientific publication, preprinting also has the potential to open new avenues of communication among researchers. For example, preprint peer review offers tremendous potential for changing the culture of scientific assessment, broadening participation, and enhancing the robustness of scholarship.

We are excited to announce the recent release of metagenomics annotations for Europe PMC publications. Thanks to the machine learning framework of Dr. Maaly Nassar for the joint EMERALD project between Europe PMC and MGnify teams, recognising metagenomics key terms in literature can now provide detailed biome descriptions for unravelling microbial implications in a variety of environmental-, engineered- and host-associated phenomena.