Toxicity continues to be a significant factor contributing to the failure of medication candidates in drug development, leading to the exorbitant expenses associated with the drugs that successfully reach the market ( ). For example, it has been observed that a significant proportion, specifically 90%, of medication candidates that have undergone clinical trials experience failure during phases I, II, or III of the trial process or throughout the process of drug licensure ( ). For example, clinical trials have revealed that uncontrollable toxicity accounts for 90% of medication development failures ). Despite receiving approval, some medications are subsequently pulled from the market due to their shown health concerns. This phenomenon engenders a decline in trust and assurance within the healthcare sector among patients, healthcare practitioners, investors, and regulatory bodies ( ). In the given context, the utilisation of computational toxicity prediction proves to be highly beneficial during the initial phases of drug exploration, as it enables the exclusion of compounds that possess a substantial likelihood of experiencing failure in clinical trials ( ). Figure : An example of ML application in RNA-seq data's toxicity prediction RNA-seq data's toxicity prediction: a deep learning method using RNA-seq data to precisely project toxicity. This complex model uses deep learning techniques to estimate toxicity levels accurately, therefore offering important data on possible harmful effects and helping to produce safer drugs and chemicals ( ).