Abstract
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discoveryresearch, requires molecular representation. Previous reports have demonstratedthat machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates andcan be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses therecent updates in AI tools as cheminformatics application in medicinal chemistry for thedata-driven decision making of drug discoveryand challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.