Artificial intelligence (AI) has become a promising tool for the discovery of pharmaceuticals, as it can enhance the probabilities of success and adds greater precision, speed and profitability to a process with high rates of failure, extensive development periods and high costs.
On this occasion, we discuss a process known as virtual screening. This refers to digital solutions that are developed to identify new molecules for specific cellular targets, thus accelerating the discovery of new pharmaceuticals. To date, the lack of available protein structures and the lack of diversity of the collections of compounds has prevented a more generalized use of this approach. Currently, the availability of extensive and diverse virtual chemical libraries and access to the structures of nearly all proteins represent two fundamental advances that make the successful adoption of virtual screening possible. Furthermore, the replacement of classical coupling tools with coupling procedures based on machine learning will unleash the full potential of virtual screening, making the discovery of pharmaceuticals more efficient, profitable and affordable for many companies in the biotechnology sector with modest budgets.