Recent Advancements in Computational Drug Design Algorithms through Machine Learning and Optimization

Abstract

The goal of drug discovery is to uncover new molecules with specific chemical properties that can be used to cure diseases. With the accessibility of machine learning techniques, the approach used in this search has become a significant component in computer science in recent years. To meet the Precision Medicine Initiative’s goals and the additional obstacles that they have created, it is vital to develop strong, consistent, and repeatable computational approaches. Predictive models based on machine learning are becoming increasingly crucial in preclinical investigations. In discovering novel pharmaceuticals, this step substantially reduces expenses and research times. The human kinome contains various kinase enzymes that play vital roles through catalyzing protein phosphorylation. Interestingly, the dysregulation of kinases causes various human diseases, viz., cancer, cardiovascular disease, and several neuro-degenerative disorders. Thus, inhibitors of specific kinases can treat those diseases through blocking their activity as well as restoring normal cellular signaling. This review article discusses recent advancements in computational drug design algorithms through machine learning and deep learning and the computational drug design of kinase enzymes. Analyzing the current state-of-the-art in this sector will offer us a sense of where cheminformatics may evolve in the near future and the limitations and beneficial outcomes it has produced. The approaches utilized to model molecular data, the biological problems addressed, and the machine learning algorithms employed for drug discovery in recent years will be the emphasis of this review.