Computational Medicinal Chemistry and Cheminformatics

EasyChair Preprint 15060

Abstract

Computational medicinal chemistry and cheminformatics have emerged as pivotal disciplines in the drug discovery process, leveraging advanced computational techniques to facilitate the design, optimization, and analysis of chemical compounds. This review highlights the integration of cheminformatics tools with molecular modeling, virtual screening, and quantitative structure-activity relationship (QSAR) methodologies to enhance the efficiency and effectiveness of drug development. We discuss the role of machine learning algorithms and artificial intelligence in predicting biological activity and improving lead compound identification. Additionally, the significance of data mining in cheminformatics is emphasized, showcasing how large chemical databases can be utilized to derive meaningful insights for compound prioritization. By streamlining the drug design process, these computational approaches not only reduce the time and cost associated with traditional methods but also expand the potential for discovering novel therapeutics. Future directions in the field are also explored, including the need for more robust predictive models and the integration of experimental data to refine computational predictions. Overall, the synergy between computational medicinal chemistry and cheminformatics represents a transformative force in modern drug discovery, with the potential to revolutionize the pharmaceutical landscape.

Keyphrases: BET, Bromodomain, Epigenetics, inhibitor, kinase

@booklet{EasyChair:15060,
  author    = {Kaledio Potter and Axel Egon and Abram Gracias},
  title     = {Computational Medicinal Chemistry and Cheminformatics},
  howpublished = {EasyChair Preprint 15060},
  year      = {EasyChair, 2024}}