Cheminformatic Analysis of Antimalarial Chemical Space Illuminates Therapeutic Mechanisms and Offers Strategies for Therapy Development

Screening Marine Natural Products for New Drug Leads against Trypanosomatids and Malaria

Neglected Tropical Diseases (NTD) represent a serious threat to humans, especially for those living in poor or developing countries. Almost one-sixth of the world population is at risk of suffering from these diseases and many thousands die because of NTDs, to which we should add the sanitary, labor and social issues that hinder the economic development of these countries. Protozoan-borne diseases are responsible for more than one million deaths every year. Visceral leishmaniasis, Chagas disease or sleeping sickness are among the most lethal NTDs. Despite not being considered an NTD by the World Health Organization (WHO), malaria must be added to this sinister group. Malaria, caused by the apicomplexan parasite Plasmodium falciparum, is responsible for thousands of deaths each year. The treatment of this disease has been losing effectiveness year after year. Many of the medicines currently in use are obsolete due to their gradual loss of efficacy, their intrinsic toxicity and the emergence of drug resistance or a lack of adherence to treatment. Therefore, there is an urgent and global need for new drugs. Despite this, the scant interest shown by most of the stakeholders involved in the pharmaceutical industry makes our present therapeutic arsenal scarce, and until recently, the search for new drugs has not been seriously addressed. The sources of new drugs for these and other pathologies include natural products, synthetic molecules or repurposing drugs. The most frequent sources of natural products are microorganisms, e.g., bacteria, fungi, yeasts, algae and plants, which are able to synthesize many drugs that are currently in use (e.g. antimicrobials, antitumor, immunosuppressants, etc.). The marine environment is another well-established source of bioactive natural products, with recent applications against parasites, bacteria and other pathogens which affect humans and animals. Drug discovery techniques have rapidly advanced since the beginning of the millennium. The combination of novel techniques that include the genetic modification of pathogens, bioimaging and robotics has given rise to the standardization of High-Performance Screening platforms in the discovery of drugs. These advancements have accelerated the discovery of new chemical entities with antiparasitic effects. This review presents critical updates regarding the use of High-Throughput Screening (HTS) in the discovery of drugs for NTDs transmitted by protozoa, including malaria, and its application in the discovery of new drugs of marine origin.

Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis

Most chemical experiments are planned by human scientists and therefore are subject to a variety of human cognitive biases¹, heuristics² and social influences³. These anthropogenic chemical reaction data are widely used to train machine-learning models⁴ that are used to predict organic⁵ and inorganic^6,7 syntheses. However, it is known that societal biases are encoded in datasets and are perpetuated in machine-learning models⁸. Here we identify as-yet-unacknowledged anthropogenic biases in both the reagent choices and reaction conditions of chemical reaction datasets using a combination of data mining and experiments. We find that the amine choices in the reported crystal structures of hydrothermal synthesis of amine-templated metal oxides⁹ follow a power-law distribution in which 17% of amine reactants occur in 79% of reported compounds, consistent with distributions in social influence models^10-12. An analysis of unpublished historical laboratory notebook records shows similarly biased distributions of reaction condition choices. By performing 548 randomly generated experiments, we demonstrate that the popularity of reactants or the choices of reaction conditions are uncorrelated to the success of the reaction. We show that randomly generated experiments better illustrate the range of parameter choices that are compatible with crystal formation. Machine-learning models that we train on a smaller randomized reaction dataset outperform models trained on larger human-selected reaction datasets, demonstrating the importance of identifying and addressing anthropogenic biases in scientific data.