Quantum mechanical-based strategies in drug discovery: Finding the pace to new challenges in drug design

被引:17
作者
Ginex, Tiziana [1 ]
Vazquez, Javier [1 ,2 ,3 ]
Estarellas, Carolina [2 ,4 ]
Luque, F. Javier [2 ,3 ,4 ]
机构
[1] Parc Cientif Barcelona PCB, Pharmacelera, Baldiri Reixac 4-8, Barcelona 08028, Spain
[2] Univ Barcelona, Inst Biomed IBUB, Dept Nutr Ciencies Alimentacio & Gastron, Santa Coloma De Gramenet 08921, Spain
[3] Inst Biomed IBUB, Santa Coloma De Gramenet 08921, Spain
[4] Inst Quim Teor & Computac IQTCUB, Santa Coloma De Gramenet 08921, Spain
关键词
BINDING-AFFINITY PREDICTION; PROTEIN; IDENTIFICATION; GENERATION; IMPACT;
D O I
10.1016/j.sbi.2024.102870
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The expansion of the chemical space to tangible libraries containing billions of synthesizable molecules opens exciting opportunities for drug discovery, but also challenges the power of computer-aided drug design to prioritize the best candidates. This chemically accurate properties, but subject to small-sized systems. Preserving accuracy while optimizing the computational cost is at the heart of many efforts to develop high-quality, efficient QM-based strategies, reflected in refined algorithms and computational approaches. The design of QM-tailored physics-based force fields and the coupling of QM with machine learning, in conjunction with the computing performance of supercomputing resources, will enhance the ability to use these methods in drug discovery. The challenge is formidable, but we will undoubtedly see impressive advances that will define a new era.
引用
收藏
页数:9
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