Advances in Activity/Property Prediction from Chemical Structures

Abstract

Recent technological advancement in AI modeling of molecular property databases has significantly expanded the opportunities for drug design and development. Quantitative structure-activity relationships (QSARs) are shown to provide more accurate predictions with regards to biological activity as well as toxicological assessment. By using a combination of in-silico models or by combining disparate structure-activity databases, researchers have been able to improve accuracy for a variety of drug discovery and analysis methods, generating viable compounds, which in certain cases, can be synthesized and further studied in vitro to find candidates for potential development. Additionally, the development of compounds of determined toxicology can be discontinued earlier, allowing alternative routes to be evaluated, preventing wasted time and resources. Although the progress that has been made is tremendous, expert review is still necessary for most in-silico generated predictions. Regardless, the scientific community continues to move ever closer to completely automated drug discovery and evaluation.

Keywords:

• Quantitative structure activity relationships
• artificial intelligence
• chemometrics
• physicochemical properties
• pharmaceuticals analysis

Author contributions

All authors have approved the final version of the manuscript.

Acknowledgment

Ohio University is thanked for providing the electronic resources that made this review possible.