Small organic molecules underpin modern life, from medicines and flavours to advanced materials. Much of this functional diversity comes from shape: modest changes in a molecule's 3D structure can completely change its properties.
Since its release in 2021, this repository has become a bedrock in discovery and a first port of call for research projects that try to understand life at the molecular level. But previous iterations of the database lacked predictions of how proteins form complexes, which can be indispensable for their function.
Central to HR is the RAD51 recombinase, whose assembly into a nucleoprotein filament is governed by five RAD51 paralogs (RAD51B, RAD51C, RAD51D, XRCC2, XRCC3). Mutations in any of these proteins predispose individuals to multiple cancers or genetic disorders. These paralogs are thought to form two functionally separate complexes, BCDX2 and CX3, that act independently at different stages of HR.
Scientists in the laboratory of Rendong Yang, PhD, associate professor of Urology, have developed a new large language model that can interpret transcriptomic data in cancer cell lines more accurately than conventional approaches, as detailed in a recent study published in Nature Communications. Long-read RNA sequencing technologies have transformed transcriptomics research by detecting complex RNA splicing and gene fusion events that have often been missed by conventional short-read RNA-sequencing methods.
Now, researchers have created an artificial-intelligence system that vastly simplifies and accelerates the process of chemical synthesis. The system, which is called MOSAIC and is described in a study published in Nature on 19 January, recommended conditions that researchers were able to use to generate 35 compounds with the potential to become products like pharmaceuticals, agrochemicals or cosmetics without needing to do any further trawling or tweaking.
Biology is undergoing a transformation. After centuries of studying life as it evolves naturally, researchers are now using a combination of computation and genome engineering to intervene, generating new proteins and even whole bacteria from scratch. The use of artificial-intelligence tools to design biological components, an approach known as generative biology, is set to turbocharge this area of research. Just last year, scientists used AI-assisted design to produce artificial genes that can be expressed in mammalian cells.
The exponential growth of scientific literature presents an increasingly acute challenge across disciplines. Hundreds of thousands of new chemical reactions are reported annually, yet translating them into actionable experiments becomes an obstacle1,2. Recent applications of large language models (LLMs) have shown promise3,4,5,6, but systems that reliably work for diverse transformations across de novo compounds have remained elusive. Here we introduce MOSAIC (Multiple Optimized Specialists for AI-assisted Chemical Prediction), a computational framework that enables chemists to harness the collective knowledge of millions of reaction protocols.
Structural biology is essential for understanding diseases and for developing drugs and vaccines. Africa has few specialists in this field, owing to limited infrastructure, training and mentorship opportunities - despite the efforts of non-profit organizations such as BioStruct-Africa, which I co-founded. doi: https://doi.org/10.1038/d41586-026-00072-3Competing Interests E.N. received a 2024 Google Award for a socially impactful project enabled by AlphaFold and Google DeepMind sponsorship in 2025 to support the BioStruct-Africa structural-biology training event (series 6). E.N. is also supported by a Wellcome Trust award (grant number 222999/Z/21/Z).
Calling nanoscientists: your field needs you to try to replicate a landmark finding that quantum dots can act as biosensors inside living cells. As part of the first large-scale effort in the physical sciences to tackle the reproducibility crisis, researchers in France and the Netherlands are offering funds and resources in exchange for a few months of work. "We are trying to use
