Parallel Flexible Molecular Docking in Computational Chemistry on High Performance Computing Clusters

The main objective in pharmaceutical research is development of novel drugs with improved biological effect in specifically afflicted organisms. A common practice in drug design focuses on systematic organic derivatization of chemical structures exhibiting certain biological activity and subsequent biological in vitro evaluation of the resulted benefits. However, this classical approach can be more or less classified as a chance drug discovery, being very arduous, expensive and time consuming. Nowadays, a lot of enthusiasm is given to rationally oriented drug research techniques like computer-aided drug design, virtual screening, bioinformatics, chemometrics, quantitative structure-activity relationships, etc. In the present article, we deal with designing a high performance computing (HPC) support for flexible molecular docking (FMD) which can be beneficially utilized in structure-based virtual screening (SBVS). The principles of FMD are briefly introduced and a solution combining message passing interface (MPI) with multithreading is proposed. The merits (e.g. availability, scalability, performance) of MPI-HPC enhanced SBVS/FMD are compared with other HPC techniques utilized for novel lead structures discovery in medicinal chemistry.

[1]  D Horvath,et al.  A virtual screening approach applied to the search for trypanothione reductase inhibitors. , 1997, Journal of medicinal chemistry.

[2]  Kamil Kuca,et al.  HPC Cloud Technologies for Virtual Screening in Drug Discovery , 2015, ACIIDS.

[3]  Xiaohua Zhang,et al.  Message passing interface and multithreading hybrid for parallel molecular docking of large databases on petascale high performance computing machines , 2013, J. Comput. Chem..

[4]  Hesham H. Ali,et al.  Applications of High Performance Computing in Bioinformatics, Computational Biology and Computational Chemistry , 2015, IWBBIO.

[5]  A Lavecchia,et al.  Virtual screening strategies in drug discovery: a critical review. , 2013, Current medicinal chemistry.

[6]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[7]  J M Blaney,et al.  A geometric approach to macromolecule-ligand interactions. , 1982, Journal of molecular biology.

[8]  Thomas Stützle,et al.  Accelerating Molecular Docking Calculations Using Graphics Processing Units , 2011, J. Chem. Inf. Model..

[9]  Chee Keong Kwoh,et al.  QuickVina: Accelerating AutoDock Vina Using Gradient-Based Heuristics for Global Optimization , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[10]  Krzysztof Kuczera,et al.  Molecular modeling of peptides. , 2015, Methods in molecular biology.

[11]  Paul D Lyne,et al.  Structure-based virtual screening: an overview. , 2002, Drug discovery today.

[12]  José L. Abellán,et al.  Enhancing the Parallelization of Non-bonded Interactions Kernel for Virtual Screening on GPUs , 2015, IWBBIO.

[13]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[14]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[15]  Andy J. Keane,et al.  Meta-Lamarckian learning in memetic algorithms , 2004, IEEE Transactions on Evolutionary Computation.