Artificial intelligence: Machine learning approach for screening large database and drug discovery

Antiviral Res. 2023 Nov 5:105740. doi: 10.1016/j.antiviral.2023.105740. Online ahead of print.ABSTRACTRecent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer aided drug discovery. It has a prominent ability in screening drug targets from large molecular databases. Recently, a number of web servers have developed for quickly screening publicly accessible chemical databases. In a nutshell, deep learning algorithms and artificial neural networks have modernised the field. Several drug discovery processes have used machine learning and deep learning algorithms, including peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modelling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Although there are presently a wide variety of data-driven AI/ML tools available, the majority of these tools have, up to this point, been developed in the context of non-communicable diseases like cancer, and a number of obstacles have prevented the translation of these tools to the discovery of treatments against infectious diseases. In this review various aspects of AI and ML in virtual screening of large databases were discussed. Here, with an empha...

Source: Antiviral Research - November 7, 2023 Category: Virology Authors: Prachi P Parvatikar Sudha Patil Kedar Khaparkhuntikar Shruti Patil Pankaj K Singh R Sahana Raghavendra V Kulkarni Anjanapura V Raghu Source Type: research