Sustainability in Drug Discovery Through Artificial Intelligence and Big Data

Medicines serve an indispensable and profoundly beneficial function in society, and their significance and utilization are anticipated to expand significantly owing to the continuous growth and aging of the global population. Nevertheless, the heightened utilization of medicines also amplifies environmental exposure, placing strain on ecosystems and adversely affecting long-term health. This can manifest in various ways, such as the development of reproductive disorders and the emergence of antibiotic resistance. The concept of sustainability has remained a topic of widespread discussion and debate. It typically entails meeting present needs without sacrificing those of future generations, while also respecting the boundaries and limitations of the planet. This often involves adopting ethical and environmentally friendly practices, commonly referred to as ‘green’ practices. The pharmaceutical and biotechnology industries are starting to embrace these practices to mitigate their environmental footprint on the planet. However, this transformation is intricate and multifaceted, as there is no straightforward checklist of solutions. Instead, various initiatives are being undertaken to enhance conditions and alleviate existing negative impacts. In this context, some sustainability principles applicable to the drug discovery process are proposed. Here we ensure a synoptic of one of these principles, the use of artificial intelligence and big data at different phases of drug discovery.