Integration of computational data science (CDS) into the university curriculum offers several advantages for students, faculty and the institution. This article discusses the benefits to students of introducing CDS into the university curriculum with a focus on developing skills in cheminformatics, data analysis, structure–activity relationships, modelling and simulation. Moreover, CDS can enable students to engage with complex chemical and toxicological data in new and dynamic ways, helping them to develop a more nuanced understanding of the potential hazards and risks associated with different chemicals and substances. On the other hand, it can foster greater collaboration between students and faculty and with external partners in industry and government. This can lead to the development of more effective and efficient toxicological testing methods and tools to screen chemicals for potential hazards and aid the development of environmentally friendly chemicals. Overall, the integration of CDS into the university curriculum will help prepare the next generation of scientists giving them a competitive edge to make considerable contributions to green chemistry, designing safer chemicals and non-animal testing methods. It will enable them to tackle modern challenges facing society including identifying safer and more sustainable chemicals and predicting the health and environmental impacts of novel chemical substances.

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Release of potentially persistent, bioaccumulative and inherently toxic chemicals into the environment may cause adverse effects to plants and animals. Hundreds of thousands of chemicals are manufactured each year and experimentally assessing them for these parameters is costly, time consuming and requires animal testing. To address these concerns there has been a paradigm shift in adoption of computational chemistry and toxicology methods for assessing environmental risks posed by such chemicals. These computer-based methods harness the power of fast processors, high speed internet, statistical methods, and curated toxicological databases to fulfil this need. In this work a summary of selected publicly available computational models and databases is presented. Global regulatory agencies apply these predictive models to support their decisions. Indian regulatory bodies too could benefit from this exercise in identifying chemicals of ecotoxicological concern and in taking an appropriate regulatory decision thereby protecting the environment.