A deep-learning approach for identifying prospective chemical hazards.

With the aim of helping to set safe exposure limits for the general population, various techniques have been implemented to conduct risk assessments for chemicals and other environmental stressors; however, none of these tools facilitate the identification of completely new chemicals that are likely hazardous and elicit an adverse biological effect. Here, we detail a novel in silico, deep-learning framework that is designed to systematically generate structures for new chemical compounds that are predicted to be chemical hazards. To assess the utility of the framework, we applied the tool to four endpoints related to environmental toxicants and their impacts on human and animal health: (i) toxicity to honeybees, (ii) immunotoxicity, (iii) endocrine disruption via ER-α antagonism, and (iv) mutagenicity.

The in silico identification of novel broad-spectrum antidotes for poisoning by organophosphate anticholinesterases.

Owing to their potential to cause serious adverse health effects, significant efforts have been made to develop antidotes for organophosphate (OP) anticholinesterases, such as nerve agents. To be optimally effective, antidotes must not only reactivate inhibited target enzymes, but also have the ability to cross the blood-brain barrier (BBB). Progress has been made toward brain-penetrating acetylcholinesterase reactivators through the development of a new group of substituted phenoxyalkyl pyridinium oximes.

Adapting physiologically-based pharmacokinetic models for machine learning applications.

Both machine learning and physiologically-based pharmacokinetic models are becoming essential components of the drug development process. Integrating the predictive capabilities of physiologically-based pharmacokinetic (PBPK) models within machine learning (ML) pipelines could offer significant benefits in improving the accuracy and scope of drug screening and evaluation procedures. Here, we describe the development and testing of a self-contained machine learning module capable of faithfully recapitulating summary pharmacokinetic (PK) parameters produced by a full PBPK model, given a set of input drug-specific and regimen-specific information.

The in silico identification of novel broad-spectrum antidotes for poisoning by organophosphate anticholinesterases.

Because of their potential to cause serious adverse health effects, significant efforts have been made to develop antidotes for organophosphate (OP) anticholinesterases, such as nerve agents. To be optimally effective, antidotes must not only reactivate inhibited target enzymes, but also have the ability to cross the blood brain barrier (BBB). Progress has been made toward brain-penetrating acetylcholinesterase reactivators through the development of a new group of substituted phenoxyalkyl pyridinium oximes.