Anthropogenic climate change is already affecting the marine plankton populations present in the western Mediterranean Sea. This is the result of a recent study that warns on the increasing surface ocean temperature lowering primary production, which has negative impacts on plankton communities and marine biodiversity.

The studied records from the 10th century up to the present in the the Balearic and the Alboran seas focus on a model group of marine calcifying zooplankton known as planktic foraminifera. Planktic foraminifera are single celled organisms with a shell made of calcium carbonate commonly found in the sedimentary record. They live in the upper ocean, responding sensitively to climate and environmental changes. 

The anthropogenic warming occurs more abruptly in the Mediterranean region compared to the global average, affecting marine plankton communities. Since 1880, atmospheric warming has been greater, accelerating the increase of sea surface temperature. Before anthropogenic carbon dioxide (CO₂) emissions led to enhanced ocean warming (prior to 1880, according to records), alterations in planktic foraminifera were mainly driven by natural variability. In contrast, during the past ca. 150 years, unprecedented anthropogenic warming of the western Mediterranean Sea has reduced the planktic foraminiferal stock. At the same time, changes in species composition are indicating that the biological productivity of the western Mediterranean diminished.

The results of the study can be seen as a sign of reduced marine productivity caused by anthropogenic warming. A less productive Mediterranean Sea would affect the food web complexity and fish stocks as well as biodiversity, causing a degradation of ecosystem services in the Mediterranean. Together with overfishing, a reduced marine productivity further threatens the rapidly changing Mediterranean Sea ecosystems and natural resources, highlighting once again the need of protecting this sea, adapting and first of all addressing the mitigation of climate change.

Small molecules are an excellent tool to probe biological functions and, indeed, they are the main asset of pharmaceutical companies. However, they have received a limited attention by academic researchers during the ‘omics revolution’ since, contrary to gene and protein knowledge, compound data are scattered and diverse, making them inaccessible to most researchers and not suited to standard statistical analyses. Often, the only way to approach the characterization of a compound is to assume it will have the same activity as compounds with similar chemical properties (i.e. the so-called ‘similarity principle’).

The broad release of bioactivity data has led to the realization that the similarity principle applies beyond chemical properties (i.e. molecules eliciting similar side-effects tend to share the mechanism of action, even when their chemical structures appear to be unrelated), suggesting that ‘biological’ similarities offer an alternative means to functionally characterize small molecules. Unfortunately, there is no blueprint to compare the biological profiles of small molecules, since bioactivity data come expressed in formats that are not adapted to common similarity metrics. The Chemical Checker (CC) provides processed, harmonized and integrated bioactivity data on ~1M small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable for modern machine learning.