Answers are grounded in this paper and cite their sources.
Computational or computer aided drug design is method of using computational perspectives in identifying, analyzing, developing and discovery of drugs and related biologically active compounds. Though the thought of computational drug discovery was started in 1970s, it was rather slow in early years but it played an important role in discovery of drugs in past three decades. Now it was thought to be an answer for industrial burdens in drug discovery. Conventional drug discovery is a long and expensive process. It may take years to decades before releasing the drug in to the market. If a new candidate fails in ADME properties or if it produces abnormal toxicity in humans, we cannot release the drug into the market. The time and money spent on its development turns waste. Due to this, pharmaceutical industry suffers huge burden. To avoid this late stage attrition we can make use of computational tools. The ADMET predictor tools can be better used to predict the properties of the drug in early and therefore time and money can be saved. This article reviews different computational tools used frequently in prediction of ADMET properties of new entities.
No takes yet. Share an insight, caveat, or question.
Kumar et al. (2018) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: