Comment

Viewing protein evolution through the lens of complex systems theory may offer new insights into the principles driving biological adaptation. In this Comment we explore how characteristics such as sensitivity to initial conditions and self-organization are observed in protein evolution and discuss how machine learning can help model these dynamics to guide protein engineering.

Orally bioavailable, high molecular weight macrocyclic peptides that inhibit difficult-to-drug protein–protein interactions are of high therapeutic value, and rules for their design were proposed recently. Here, we emphasize the danger of rules that provide a false impression of the lipophilicity required of a clinical candidate.

RAS proteins, central drivers of cancer, appeared ‘undruggable’ for almost 30 years. Here we provide a personal perspective on the effort leading to our initial report of KRAS^G12C inhibitors in 2013, and the decade of discoveries that followed.

The inaugural CRISPR-based drug Casgevy has been approved by several medical agencies, with other CRISPR-based therapies currently in clinical trials. Although there are technological hurdles to overcome, chemical biology has a vital role in developing recent breakthroughs in base editing, prime editing and epigenetic editing into future treatments.

AlphaFold is a breakthrough in protein structure prediction, but limitations in its application to computation- and structure-guided drug discovery remain. As with structure prediction, public-domain data and benchmarking initiatives will be essential to advance the field of computational drug design.

Since its proposition in 2002, the Division of Chemical Biology in the Department of Chemical Sciences at the National Natural Science Foundation of China has implemented a range of constructive initiatives to break down traditional boundaries between chemistry and biology and to further bridge these two fields, in order to boost the development of chemical biology in China.

Protein glycation has long-been considered a toxic consequence of carbohydrate metabolism. Yet recent evidence demonstrates tight regulation for these non-enzymatic post-translational modifications, pointing to a broader role in cell biology rather than simply serving as a biomarker for toxicity.

Biased signaling gives hormones, probes or drugs distinct functional outcomes via the same receptor. The Biased Signaling Atlas (https://BiasedSignalingAtlas.org) provides a community hub with data and tools to advance this paradigm, which may yield safer and more potent drugs.

Artificial intelligence (AI) is poised to transform therapeutic science. Therapeutics Data Commons is an initiative to access and evaluate AI capability across therapeutic modalities and stages of discovery, establishing a foundation for understanding which AI methods are most suitable and why.

Ferroptosis is a regulated form of non-apoptotic cell death implicated in pathological settings. To be exploited clinically, ferroptosis requires reagents that unequivocally detect ferroptosis in human and animal tissues. Such tools may enable development of ferroptosis-based medicines for diverse diseases.

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

Plant biologists have recognized the potential in using small molecules identified from chemical libraries to provide insights into biological questions relevant to plants. However, the classical genetics mindset still predominant among plant scientists should evolve to embrace cross-disciplinary chemical genetics projects that will benefit future plant research.