Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will disc...
Generating considerable amounts of industrial waste requires rethinking chemistry for circularity in a broader picture. We discuss the study by Wołos et al. (2022) showing that the critical application of artificial intelligence on chemical reactivit...
Over the last decade, the artificial intelligence (AI) has undergone a revolution that is poised to transform the economy, society, and science. The pace of progress is staggering, and problems that seemed intractable just a few years ago have now be...
Large and complex datasets have made artificial intelligence (AI) an invaluable tool for discovery across biological research. We asked experts how AI has impacted their work. Their experiences and perspectives offer thoughtful insights into potentia...
This Perspective explores the application of machine learning toward improved diagnosis and treatment. We outline a vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health...
Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed pr...
Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipelin...
The importance of genomic sequence context in generating transcriptome diversity through RNA splicing is independently unmasked by two studies in this issue (Jaganathan et al., 2019; Baeza-Centurion et al., 2019).
A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic...
Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovati...