Molecular Docking: Shifting Paradigms in Drug Discovery

被引:1452
作者
Pinzi, Luca [1 ]
Rastelli, Giulio [1 ]
机构
[1] Univ Modena & Reggio Emilia, Dept Life Sci, Via Giuseppe Campi 103, I-41125 Modena, Italy
关键词
molecular docking; drug discovery; drug repurposing; reverse screening; target fishing; polypharmacology; adverse drug reactions; MACHINE LEARNING APPROACH; PROTEIN INVERSE DOCKING; HIGH-THROUGHPUT DOCKING; WEB SERVER; BINDING-AFFINITY; REVERSE DOCKING; TARGET IDENTIFICATION; POTENTIAL TARGETS; SCORING FUNCTIONS; FLEXIBLE DOCKING;
D O I
10.3390/ijms20184331
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
Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.
引用
收藏
页数:23
相关论文
共 207 条
[1]   High-throughput docking for lead generation [J].
Abagyan, R ;
Totrov, M .
CURRENT OPINION IN CHEMICAL BIOLOGY, 2001, 5 (04) :375-382
[2]   Conformational Selection and Induced Fit Mechanisms in the Binding of an Anticancer Drug to the c-Src Kinase [J].
Agnese Morando, Maria ;
Saladino, Giorgio ;
D'Amelio, Nicola ;
Pucheta-Martinez, Encarna ;
Lovera, Silvia ;
Lelli, Moreno ;
Lopez-Mendez, Blanca ;
Marenchino, Marco ;
Campos-Olivas, Ramon ;
Gervasio, Francesco Luigi .
SCIENTIFIC REPORTS, 2016, 6
[3]   Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening [J].
Ain, Qurrat Ul ;
Aleksandrova, Antoniya ;
Roessler, Florian D. ;
Ballester, Pedro J. .
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE, 2015, 5 (06) :405-424
[4]   Combining docking and molecular dynamic simulations in drug design [J].
Alonso, Hernan ;
Bliznyuk, Andrey A. ;
Gready, Jill E. .
MEDICINAL RESEARCH REVIEWS, 2006, 26 (05) :531-568
[5]   Ensemble Docking in Drug Discovery [J].
Amaro, Rommie E. ;
Baudry, Jerome ;
Chodera, John ;
Demir, Ozlem ;
McCammon, J. Andrew ;
Miao, Yinglong ;
Smith, Jeremy C. .
BIOPHYSICAL JOURNAL, 2018, 114 (10) :2271-2278
[6]   Heat shock protein 90 and serine/threonine kinase B-Raf inhibitors have overlapping chemical space [J].
Anighoro, A. ;
Pinzi, L. ;
Marverti, G. ;
Bajorath, J. ;
Rastelli, G. .
RSC ADVANCES, 2017, 7 (49) :31069-31074
[7]   Three-Dimensional Similarity in Molecular Docking: Prioritizing Ligand Poses on the Basis of Experimental Binding Modes [J].
Anighoro, Andrew ;
Bajorath, Juergen .
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2016, 56 (03) :580-587
[8]   Polypharmacology: Challenges and Opportunities in Drug Discovery [J].
Anighoro, Andrew ;
Bajorath, Juergen ;
Rastelli, Giulio .
JOURNAL OF MEDICINAL CHEMISTRY, 2014, 57 (19) :7874-7887
[9]   A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking [J].
Ballester, Pedro J. ;
Mitchell, John B. O. .
BIOINFORMATICS, 2010, 26 (09) :1169-1175
[10]   Automated systems for protein crystallization [J].
Bard, J ;
Ercolani, K ;
Svenson, K ;
Olland, A ;
Somers, W .
METHODS, 2004, 34 (03) :329-347