Chemical space modelling has great importance in unveiling and visualising latent
information, which is critical in predictive toxicology related to drug discovery process. While …

The understanding of toxicity is of paramount importance to human health and
environmental protection. Quantitative toxicity analysis has become a new standard in the …

Predictive modeling of toxicity is a crucial step in the drug discovery pipeline. It can help filter
out molecules with a high probability of failing in the early stages of de novo drug design …

Drug-induced liver injury (DILI) is the principal reason for failure in developing drug
candidates. It is the most common reason to withdraw from the market after a drug has been …

Advances in the drug discovery research substantially depend on in silico methods and
techniques that capitalize on experimental data to enable the accurate property/activity …

Screening new compounds for potential bioactivities against cellular targets is vital for drug
discovery and chemical safety. Transcriptomics offers an efficient approach for assessing …

With the need to investigate alternative approaches and emerging technologies in order to
increase drug efficacy and reduce adverse drug effects, network biology offers a novel way …

We previously demonstrated that the Connectivity Map (CMap)(Lamb et al., 2006) concept
can be successfully applied to a predictive toxicology paradigm to generate meaningful MoA …

Teratogenic drugs can lead to extreme fetal malformation and consequently critically
influence the fetus's health, yet the teratogenic risks associated with most approved drugs …

This article demonstrates how to create Chemical Space Networks (CSNs) using a Python
RDKit and NetworkX workflow. CSNs are a type of network visualization that depict …