Department of Pharmaceutical Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy.
Institute of Computational Chemistry and Catalysis (IQCC) and Department of Chemistry, Faculty of Sciences, University of Girona, C/M. A. Capmany 69, 17003 Girona, Spain.
Molecules. 2021 Nov 22;26(22):7061. doi: 10.3390/molecules26227061.
Medicinal chemistry is facing new challenges in approaching precision medicine. Several powerful new tools or improvements of already used tools are now available to medicinal chemists to help in the process of drug discovery, from a hit molecule to a clinically used drug. Among the new tools, the possibility of considering folding intermediates or the catalytic process of a protein as a target for discovering new hits has emerged. In addition, machine learning is a new valuable approach helping medicinal chemists to discover new hits. Other abilities, ranging from the better understanding of the time evolution of biochemical processes to the comprehension of the biological meaning of the data originated from genetic analyses, are on their way to progress further in the drug discovery field toward improved patient care. In this sense, the new approaches to the delivery of drugs targeted to the central nervous system, together with the advancements in understanding the metabolic pathways for a growing number of drugs and relating them to the genetic characteristics of patients, constitute important progress in the field.
药物化学在向精准医学迈进的过程中面临着新的挑战。现在,药物化学家有了几个强大的新工具或改进的已有工具,可以帮助他们在从命中分子到临床使用药物的药物发现过程中。在新工具中,考虑将折叠中间体或蛋白质的催化过程作为发现新命中的目标的可能性已经出现。此外,机器学习是一种新的有价值的方法,可以帮助药物化学家发现新的命中。其他能力,从更好地理解生化过程的时间演化到理解遗传分析产生的数据的生物学意义,都在朝着进一步提高患者护理的药物发现领域的进展方向发展。从这个意义上说,新的方法将药物递送到中枢神经系统,以及对越来越多药物的代谢途径的理解并将其与患者的遗传特征联系起来,是该领域的重要进展。